11 Jun 1991 ... 7.4.1 Initial Origin-Destination Matrix Generation .... recommended for further
analysis and application: CONTRAM, SATURN, ...... (1 st iteration1.
CALIFORNIA PATH PROGRAM INSTITUTE OF TRANSPORTATION STUDIES UNIVERSITY OF CALIFORNIA AT BERKELEY
Model Selection and Initial Application of CONTRAM Model for Evaluating In-Vehicle Information Systems Yonnel Gardes Bruce Haldors Adolf D. May PATH Research Report UCB-ITS-PRR-91-11
This work was performed as part of the California PATH Program of the University of California, in cooperation with the State of California, Business and Transportation Agency, Department of Transportation, and the United States Department of Transportation, Federal Highway Administration. The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation.
June 1991
ISSN 1055- 1425
This paper has been mechanically scanned. Some errors may have been inadvertently introduced.
ACKNOWLEDGEMENTS
The authors would like to thank TRRL, especially, Nick Taylor, without whom much of this work would not have been possible. Additionally, the authors would like to thank the City of Los Angeles and Caltrans for their generous support and assistance. The authors would also like to thank the staff at the PATH office for all of their support and assistance, in particular Arma Bozzini and Sherry Parrish.
TABLE OF CONTENTS
EXECUTIVESUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.0
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background 1.1 1.2 . Purpose Scope and Study Approach 1.3
3
2.0
PREVIOUSRESEARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1987-1989 2.1 1989/1990 RESEARCH 2.2
5
3.0
MODEL EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . 1 CONTRAM 3.1.1 Basic Structure 3.1.2 Input Data Requirements 3.1.3 outputs 3.1.4 Demonstration 3.1.5 Reported Applications 3.2 SATURN 3.2.1 Basic Structure 3.2.2 Input Data Requirements 3.2.3 Outputs 3.2.4 Demonstration 3.2.5 References 3.3 INTEGRATION 3.3.1 Basic Structure 3.3.2 Input Data Requirements 3.3.3 outputs 3.4 MODEL SELECTION
9
4.0
FEATURES OF CONTRAM IMPORTANT TO THIS APPLICATION . . . . . . . . . . . 4 . 1 CONTRAM 5 4.1.1 Representation of Traffic 4.1.2 Assignment 4.1.3 Queue and Delay Model 4.1.4 Blocking-Back 4.1.5 Speed/Flow Relationships 4.2 COMEST 4.2.1 Principles 4.2.2 COMESTKONTRAM Relationship 4 . 3 RODIN
29
5.0
SMARTCORRIDOR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Availability of Good Data Base 5.1 5.1.1 Supply Parameters 5.1.2 Control Parameters
38
5.2 5.3 5.4
Size of the Corridor and Traffic Congestion Meeting with City of Los Angeles Department of Transportation and Caltrans Pathfinder
6.0
INITIAL MODEL APPLICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear Test Freeway Segment 6.1 Smart Corridor Freeway Modelling 6.2 , 6.2.1 Introduction 6.2.2 Bottleneck Location and Queue Length Pattern 6.2.3 Average Speeds 6.2.4 Overall Summary Measures 6.2.5 Conclusions
7.0
DESIGN OF EXPERIMENT, REFERENCE BASE ASSIGNMENT, SIMULATION AND VALIDATION . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . Design of Experiment 7.1 Reference Base Assignment and Simulation 7.2 Network Description and Calibration 7.3 Corridor Demand 7.4 7.4.1 Initial Origin-Destination Matrix Generation 7.4.2 Use of the COMEST Program 7.4.3 Final Origin-Destination Matrix Base Run Validation 7.5 7.5.1 Travel Times/Route Choice 7.5.2 Bottleneck Location/Queuing Pattern 7.5.3 Overall Corridor Wide Summary Information
42
59
8.0
INCIDENT MODELLING . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . Incident Scenario 8.1 8.1.1 Location 8.1.2 Incident Severity and Duration Incident Modelling Within CONTRAM 8.2 8.2.1 Methodology 8.2.2 Results
76
9.0
SIMULATION RESULTS UNDER SEVERE INCIDENT SCENARIO . . . . . . . . . . . Methodology: Modelling Guidance Systems in CONTRAM 9.1 Analysis Results 9.2 9.2.1 System-Wide Results 9.2.2 Benefits to Guided and Unguided Vehicles General Evaluation 9.3
85
10.0
OVERALL ASSESSMENT AND FUTURE RESEARCH . . . . . . . . . . . . . . . . . . . . 104 10.1 Suitability of CONTRAM 10.1.1 Strengths 10.1.2 Weaknesses - Suggested Improvements 10.2 Weaknesses in Study 10.3 Major Findings 10.4 Future Research
LIST OF FIGURES
10 17
3.3
OVERALL STRUCTURE OF CONTRAM & SUITE OF PROGRAMS . . . . . . . . . . . CONTRAM TEST NETWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE SIMULATION AND ASSIGNMENT PHASES OF SATURN . . . . . . . . . . . . . .
3.4
BASIC STEPS USED BY INTEGRATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
4.1 4.2 4.3
31
4.4
ITERATIVE PROCEDURE USED BY CONTRAM . . . . . . . . . . . . . . . . . . . . . . . SPEED/FLOW RELATIONSHIP IN CONTRAM . . . . . . . . . . . . . . . . . . . . . . . . . COMEST-CONTRAM RELATIONSHIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RODIN-CONTRAM RELATIONSHIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1
LOCATION MAP - SMART CORRIDOR
.............................
41
6.1 6.2
44 45 46 48
6.7
LINEAR TEST FREEWAY INFORMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . SPEED/SHOCK WAVE INFORMATION - MANUAL CALC. . . . . . . . . . . . . . . . . SPEED/SHOCK WAVE INFORMATION - CONTRAM . . . . . . . . . . . . . . . . . . . . SPEED FLOW CURVE - TEST FREEWAY (BOTTLENECK) . . . . . . . . . . . . . . . . SPEED/FLOW CURVE - TEST FREEWAY (ONE SS DOWNSTREAM) . . . . . . . . . . NETWORK FOR SANTA MONICA EASTBOUND . . . . . . . . . . . . . . . . . . . . . . . DEMAND INFORMATION - SANTA MONICA EASTBOUND . . . . . . . . . . . . . . .
6.8
QUEUING PATTERN - FREQ, CONTRAM COMPARISON . . . . . . . . . . . . . . . . .
6.9
SPEED PATTERN - FREQ, CONTRAM COMPARISON
...................
55 57
7.1 7.2
DESIGN OF EXPERIMENT . . . . . . . . . . MAPOFNETWORKMODELLED . . . . . ORIGIN-DESTINATION LOCATION MAP ORIGIN-DESTINATION MATRIX . . . . . .
............................ ............................
60 61
3.1 3.2
6.3 6.4 6.5 6.6
7.3 7.4 7.5 7.6
....... ....... ORIGIN-DESTINATION DIFFERENCE GRAPH . . . FINAL DEMAND INFORMATION . . . . . . . . . . . .
19
33 35 37
49 51 52
..................... .....................
64 65
.....................
66 69
..................... TRAVEL TIME COMPARISON (ORIGIN 1 TO DEST. 14) . . . . . . . . . . . . . . . . . . COMPARATIVE DISTANCE TRAVEL TIME COMPARISON . . . . . . . . . . . . . . . .
70
7.9 7.10
UFPASCEXAMPLEOUTPUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
QUEUING PATTERN - REFERENCE BASE ASSIGNMENT . . . . . . . . . . . . . . . . .
74
8.1
QUEUING PATTERN - SLIGHT INCIDENT (EB SANTA MONICA)
8.2
QUEUING PATTERN - SEVERE INCIDENT (EB SANTA MONICA) . . . . . . . . . . .
79 80
7.7 7.8
...........
71
8.3
QUEUING PATTERN - SLIGHT AND SEVERE INC. (FREQ) . . . . . . . . . . . . . . . .
81
9.1 9.2 9.3 9.4
PERCENT INCREASE IN AVG. SPEED (NETWORK-WIDE) SEV. INC. . . . AVERAGE TRAVEL TIME INCREASE PER VEH. SEV. INCIDENT . . . . . PERCENT REDUCTION IN TRAVEL TIME (NETWORK-WIDE) SEV. INC. PERCENT DECREASE IN TOTAL FINAL QUEUES SEV. INCIDENT . . .
88
9.5 9.6
ORIGIN-DESTINATION PAIRS EVALUATED
...... ...... ......
89 90
......
91
.........................
AVERAGE SPEED ORIGIN 5006 TO DEST. 9014
....................... AVERAGE TRAVEL TIME ORIGIN 5006 TO DEST. 9014 . . . . . . . . . . . . . . . . . .
93 95 96
9.8 9.9 9.10 9.11
AVERAGE SPEED ORIGIN 5001 TO DEST. 9015 . . . . . . . . . . . . . . . . . . . . . . . AVERAGE TRAVEL TIME ORIGIN 5001 TO DEST. 9015 . . . . . . . . . . . . . . . . . .
97 98
AVERAGE SPEED ORIGIN 5025 TO DEST. 9014 . . . . . . . . . . . . . . . . . . . . . . . AVERAGE TRAVEL TIME ORIGIN 5025 TO DEST. 9014 . . . . . . . . . . . . . . . . . .
99 100
9.12
ROUTE CHOICE - GUIDED, UNGUIDED VEHICLES
....................
101
9.7
LIST OF TABLES
2.1
PRELIMINARY SCREENING PROCESS
5.1
IDEAL SATURATION FLOW RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
6.1
RELATIONSHIP BETWEEN MIN. DIST. HEADWAY AND DENSITY . . . . . . . . . .
47
6.2 6.3 6.4
MAIN FREEWAY EVENTS (FREQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MAIN FREEWAY EVENTS (CONTRAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OVERALL NETWORK WIDE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53 54 56
7.1 7.2
CORRESPONDING TIME SLICE DEMANDS . . . . . . . . . . . . . . . . . . . . . . . . . . BASE REFERENCE ASSIGNMENT SUMMARY . . . . . . . . . . . . . . . . . . . . . . . .
67
8.1
SLIGHT INCIDENT ASSIGNMENT SUMMARY . . . . . . . . . . . . . . . . . . . . . . . .
82
8.2
SEVERE INCIDENT ASSIGNMENT SUMMARY . . . . . . . . . . . . . . . . . . . . . . . .
83
9.1 9.2
SYSTEM WIDE RESULTS - INCIDENT RUNS . . . . . . . . . . . . . . . . . . . . . . . . . AVERAGE SPEED, TRAVEL TIME - O/D PAIRS . . . . . . . . . . . . . . . . . . . . . . .
87 102
..............................
7
75
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 APPENDICES.................................................... Appendix A Appendix B Appendix C Appendix D Appendix E
111
EXECUTIVE SUMMARY
BACKGROUND
The preliminary evaluation of the potential benefits of in-vehicle information systems was conducted by an Institute of Transportation Studies research team in 1988 and 1989 using the computer programs, FREQ and TRANSYT to model the Smart Corridor in Los Angeles, California. Out of that study came recommendations for future research on the need for more realistic simulation of the interaction between the freeway and parallel arterials. A study was conducted in 1990 to assess which models were suitable to evaluate in-vehicle information systems within an integrated freeway/arterial corridor. Twenty-four models were identified as being potentially suitable. Of the 24 models identified, three were recommended for further analysis and application: CONTRAM, SATURN, and INTEGRATION. OBJECTIVES
The objectives of this study were to select a traffic assignment and simulation model, apply that model to an integrated freeway/arterial network such as the Smart Corridor in Los Angeles, California, and, using the model, make an initial evaluation of in-vehicle information systems and the applicability of the model. APPROACH
The approach consisted of first evaluating of the CONTRAM, SATURN, and INTEGRATION models and then selecting of one of the models for a detailed analysis of the features which would be best suited to this particular application. The next step was an initial application of the selected model to a generic network and then the Smart Corridor. The final steps in the study were to make an assessment of the model, present major findings of the study, and describe the potential for future research. RESULTS
After review of the CONTRAM, SATURN, and INTEGRATION models, the CONTRAM model was chosen for further evaluation. Since the CONTRAM model was primarily developed for use in the design of traffic management schemes for urban signalized arterial networks, further analysis into the models ability to model freeway congestion was necessary. In order to gain a more clear understanding of the freeway modelling characteristics within CONTRAM, several test networks were designed and evaluated and as a result problems were discovered regarding the ability of the model to accurately reflect freeway congestion. However, an analysis ensued to evaluate the potential benefits of in-vehicle 1
information systems. The results of the study should be viewed with some caution due to difftculties with the freeway modelling characteristics of CONTRAM, as well as weaknesses within the characteristics of the network and structure of the demand pattern. The results are best considered in a qualitative manner with the findings being, the more vehicles that are equipped with in-vehicle information, the better the system performance. For a severe incident condition on the freeway, as the percentage of vehicles equipped with information increases, the performance of the system improves until the system is at a level of performance that is only slightly less than that before the incident occurred.
2
1.0
INTRODUCTION
This report describes the results of the study team’s efforts to:
1.1
1)
select a traffic assignment and simulation model;
2)
apply that model to an integrated freeway/arterial network such as the Smart Corridor in Los Angeles, California; and,
3)
using the model, make an initial evaluation of in-vehicle information systems and the applicability of the model.
Background
As traffic congestion increases worldwide, attempts are being made at improving the efficiency of the existing systems through the use of information available through computers. Past research has indicated that up to $45 billion per year is lost due to excess travel time which could be recovered if there was a more efficient transportation system that used navigational systems [ 11. Several researchers have attempted to make a quantitative assessment of in-vehicle information systems [2]. Most previous studies have been network specific, i.e., the benefits of in-vehicle information systems were related only to the network in question. This holds true for this particular application as well. For this study, the integrated freeway/arterial network chosen for modelling was the Smart Corridor in Los Angeles, California.
1.2
Purpose
The purpose of this study was to select an appropriate traffic assignment and simulation to evaluate invehicle information systems. After the appropriate model was selected, an initial application to the Smart Corridor was conducted and a second and third application were undertaken to simulate an incident on the freeway. Varied percentages of in-vehicle information systems were then modelled which permitted an initial evaluation of the applicability of the model.
1.3
Scope and Study Approach
Based on previous research (Al-Deek, Martello, Sanders and May [3]), it was determined that an equilibrium model combining traffic simulation, control, and assignment was desirable for evaluating the potential benefits of in-vehicle information systems in an integrated freeway/arterial corridor. Thus, a study was begun to evaluate the models available for the task of evaluating in-vehicle information systems. This project is an extension of the original work by May in 1986 [3]. Chapter 2 of this report outlines the history and background of this and previous studies. Chapter 3 describes the evaluation of the CONTRAM, SATURN, and INTEGRATION models. Chapter 4 discusses in more detail the features in CONTRAM that were best suited for application in this study. Chapter 5 describes the Smart Corridor and the features that made it particularly attractive to be used in the modelling process. Chapter 6 describes the initial applications of the CONTRAM model to both a generic freeway segment, and the freeway segment to be used in the entire corridor. Chapter 7 outlines the design of the experiment and how a reference-base-run assignment was derived. Chapter 8 gives a description of incident modelling within CONTRAM. Chapter 9 presents the analysis findings and results, while Chapter 10 summarizes an assessment of the model and the modelling effort and discusses the potential for future research.
4
2.0
PREVIOUS RESEARCH
Since the early 1980’s much research has been conducted in the area of in-vehicle information systems [4 - 91. One of the primary objectives of much of this research has been to provide a quantitative assessment of in-vehicle information value in a real-world freeway corridor under recurring and nonrecurring congestion.
2.1
1987-1989
The history of this study dates back to 1987. PATH Research Report UCB-ITS-PRR-88-2 [3] details the initial attempts at understanding the Potential Benefits of In-Vehicle Information Systems in a Real Life Freeway Corridor under Recurring and Incident-Znduced Congestion. This initial attempt was conducted using the simulation models FREQ and TRANSYT-7F. The Santa Monica freeway corridor was simulated based on data collected by the Los Angeles Department of Transportation and Caltrans from 1984 to 1988. Since TRANSYT-7F and FREQ do not perform traffic assignment, a network model was developed called PATHNET. PATHNET was utilized to determine the travel times for the shortest path between any origin and destination point in the network or for any other path in the network. PATHNET is a prototype version of a generalized network analysis package. PATHNET prints a report listing the links in the minimum-cost path and the cumulative route cost for each link. Thus, the research team was able to assess the potential benefits by comparing travel times between different origin and destination pairs under different scenarios. The results of the study were as follows [3, 10-111: 0
under the recurring, non-incident congestion scenario, the travel time savings were generally negligible (less than three minutes for a 20-25 minute trip);
0
under the non-recurring, incident congestion scenario, travel time savings were found to be significant (greater than three minutes);
0
the greatest travel time savings occur during the time slices following the introduction of a freeway incident.
One recognized weakness with the earlier study was the fact that the user equilibrium issue was not addressed. To address this weakness, a traffic assignment model which combines traffic assignment with 5
simulation was chosen as the tool for evaluation which achieves user equilibrium through each assignment. Thus, the first objective of this study was to find a model that could model an integrated freeway/arterial network and also combine traffic assignment with simulation.
2.2
1989-1990 RESEARCH
The 1989-1990 research focused on the modelling approaches for evaluating advanced traffic control strategies and in-vehicle information systems within an integrated network of traffic signals and freeways. Efforts included a literature review of candidate freeway/arterial models. An assessment of model suitability was carried out in order to determine if any existing model would be potentially suitable, the specific modifications needed to be included in a reasonable level of effort, or the specifications that would be required for developing a new model. The approach consisted of a literature review and preliminary assessment of candidate models, an in-depth evaluation of the most promising models, and the selection of a few models for further analysis and testing. The literature review resulted in the identification of twenty-four candidate models, classified into four categories:
1)
Transportation planning models: MINUTP, Tmodel, TRANPLAN, CARS, MICROTRIPS, EMME2, MULATM;
2)
Freeway operation models: FREQ, INTRAS, MACK-FREFLO-FRECON, KRONOS, FREESIM, ROADRUNNER;
3)
Signalized network operation models: TRAFFICQ, MICRO-ASSIGNMENT, SATURN, CONTRAM, JAM;
4)
Freeway/arterial operation models: CORQlC, SCOT, TRAFLO, DYNEV, CORQCORCON, INTEGRATION.
A preliminary screening process (summarized in Table 2.1) indicated that only five of the models chosen were capable of simultaneously performing traffic assignment and traffic simulation under oversaturated conditions, which were considered as two essential features for the purposes of this study. For three of these models (INTEGRATION, SATURN and CONTRAM), an in-depth evaluation was carried out, including tabular summaries of the characteristics of each model, rating of the performance of each model and the corresponding strengths and weaknesses, and a discussion on model suitability with regard to our application. A final report describing the 1989/1990 activities was published in June 1990 [12]. It was 6
recommended that the three selected models be acquired in order to perform a hands-on experiment and assessment.
8
3.0 MODEL EVALUATION
The main features of the CONTRAM, SATURN and INTEGRATION models are highlighted in this chapter and a demonstration of SATURN and CONTRAM is described.
3.1 CONTRAM CONTRAM is a traffic assignment model developed by the Transport and Road Research Laboratory for use in the design of traffic management schemes in urban areas. CONTRAMS (Continuous Traffic Assignment model Version 5) is the latest version of the CONTRAM program which was originally written in the early 1970s. Given the traffic demands between origins and destinations for a network, it predicts routes of vehicles and flows and queues on links. It is a capacity restrained model which takes account of the interactive effects of trafftc between intersections and the variation through time of traffic conditions. In particular, CONTRAM models the build up and decay of congestion such as occurs during peak periods.
3.1.1 Basic Structure [13] The overall structure and suite of programs in CONTRAM is outlined in Figure 3.1. The inputs are the network data, the traffic demand data, and the control data. The bases of the program are the assignment process, which calculates and stores vehicle route information, and the calculation through time of the delays on links derived from the flows and queues of vehicles.
3.1.2 Input Data Requirements [13] The three major components of input to the model are the network and time data, the demand data, and the control data. The following pages outline the characteristics of these three areas. 3.1.2.1
Network and Time Data
This defines the period to be simulated and the geometric properties of the network. The following provides a description of the basic card types used in the CONTRAM model.
FIGURE 3.1 O~RAJJ., STRUCTURE OF CONTRAM 81 SUITE OF PROGRAMS INPUTS OUTPUTS Traffic Engineering and Economics parameters Link flows. queues and turning movements % saturation and blocking back
QUEUES
Journey time and distance Fuel consumption Average ‘point-to-point’ O-O speeds
Calculate time variation in Fiows, Queues and Routes an
iterarive
4
- Convergence parameters
process
Vehicle route information Summary file to UFPASC
for
J
1
User friendly data entry system Traffic and conomic assessment information Vehicle route information
0-0’5 from flow-5 Cnnstraine$marrix eszimation method
/ UFPASC I-+ 1
I
-_
User friehdiy post
-_
analysis system
10
Selective analysis Comparative tables Graphic displays Route analysis .
input
.
Card type 1 is the time card which defines the duration of the simulation period and the time intervals into which the period is divided. The maximum number of time intervals is 13 while the maximum duration of a simulation period is eight hours. Card type 2 defines the general parameters of the network. It sets the values of certain network parameters used for the estimation of storage capacity on a link, to specify signal lost time for capacity calculations and to select the separate calculation of geometric delay at intersections. Card type 3 defines the links to which an origin is connected. Card types 4, 5, and 6 define the type of control used at link junctions. Card type 4 represents an uncontrolled link. It gives detail required for an uncontrolled link: cruise time (or cruise speed, or speedflow relationship to be used), length, saturation flow, storage capacity. Card type 5 represents give-way links. Card type 6 represents signal-controlled links and has the same basic requirements as card type 4 plus percentage green and delay factors. Card type 7 defines the speed-flow relationships. Speed/flow relationships have been incorporated to be used on roads where cruise time is a significant proportion of total time, e.g. on urban freeways and other limited-access high speed urban roads. The effect of a speed/flow relationship is in addition to explicit queuing at the downstream end of the link to which it applies. The general form of speed/flow relationships used by CONTRAM consists of two linear sections of different slope. The exact form is determined by entering as data three points:
1) 2)
3)
the free speed, where flow is zero the break point, where the slope changes the capacity point, which is a point through which the second section passes.
Card type 8 is the change of mind card, which allows the user to vary values of a parameter without changing the original data cards. Card type 9 defines the vehicle classes used in the simulation. The card specifies passenger car unit equivalents and relative cruise times for each vehicle type. The model distinguishes three classes of vehicle, car, bus, and trucks. Card type 10 defines the coefficients of the fuel model, which is based on the following formula for fuel consumption per unit distance at steady speed V:
11
F=A+(B/V)+(C*V'+) Card types 11 and 12 allow the saturation flow value on a link to be varied from time interval to time interval, for example to allow the effect of an accident to be simulated. Card type 13 allows the calculation of a geometric delay due to deceleration and acceleration at an intersection. CONTRAM 5 provides the option to calculate the geometric delay explicitly for each separate turning movement. Card types 14, 15 and 16 set the speeds for turning movements out of individual links. Card type 18 defines the range of allowed destination numbers. 3.1.2.2
Traffic Demand Data
The traffic demand data specifies the flow rate during each time interval for each origin-destination movement. The traffic demand for each origin-destination movement in a network is specified as a series of flow rates (veh/h) for each time interval. For a given O-D pair, one data card is used for each classified vehicle demand (C, B or L). The card also contains: 0
0 0
the packet size, which can be generated automatically; the “straight-line” distance between the origin and destination (optional); the start-code (time of start of the first packet from the O-D demand to enter the network in the first time interval).
It is possible to model the movements of more than one demand for the same class of vehicle, between the same origin and destination, by using separate cards, or the change of mind card. The change of mind cards can be used to change specified flow rates. 3.1.2.3
Control Data
The data in the control data pack has two control functions. The first, describing the running of the program, defines the number of iterations to be carried out and the types of output required. The second provides the additional data required for signalized intersections. The data required for vehicles with fixed routes are also specified in this pack. 0
Card type 50: Maximum number of iterations
12
Selection of outputs: 0
0 0 0 0 0 0 0 0 0
Card type 51: Network summary information for assessing convergence Card type 52: Change in vehicle arrivals - convergence matrix Card type 53: Link-by-link data - all parameters Card type 54: Link-by-link values - flows, queues, queue times, average speeds Card type 55: Measure of fairness Card types 56 and 57: Output of turning movements Card type 58: Alternative units of measurement Card type 59: Alternative file units for results Card type 60: Control of algorithms (used to select variable or constant packet size) Card type 154: Selection of tables in output
Cost parameters are specified by the following card types: 0
0 0
Card type 61: Perceived cost output units Card type 62: Perceived cost functions Card types 63 and 64: Resource cost functions
The perceived cost is the cost that is perceived by drivers which they seek to minimize by their route choice. The resource cost is assumed to represent the real cost of travel and in CONTRAM, is purely an output quantity which has no effect on route choice. In CONTRAM, the functional form of both perceived and resource cost is C = Ad + Bt + C?*d which expresses cost C in terms of distance d, time t and average speed v. Additional signal data can be specified by the following card types: 0
0 0 0 0
Card type 70: Common signal coordination factor Card type 71: Signal plans - fixed cycle/fixed splits Card type 72: Signal plans - fixed cycle/optimized splits Card type 73: Signal plans - optimized cycle/optimized splits Card type 77: Intersection signal plans schedule
Fixed route data can be specified by the following card types: 0
0
Card type 81: Specification of fixed routes Card type 85: O-D movements having fixed routes 13
3.1.3 outputs [13]
There are six forms of output, any selection of which can be called by the appropriate card types 51 to 56. 3.1.3.1
Summary Information
The data provided for each time-slice are as follows: 0
0 0 0 0 0 3.1.3.2
Total Journey-time (veh.h) Total Distance Travelled (vehkms) Overall Network Speed (km/h) Total Final Queues (veh) Fuel Consumption (litres) Total Link Counts (veh) Convergence Monitor
The purpose of these printouts is to provide data for assessing convergence. The convergence indicators are, for all iterations, the total journey-time, the total distance travelled, and the changes in initial queues plus arrivals on links. 3.1.3.3
Link-by-Link Values (All Parameters)
These data contain, for each time slice, the values of the following parameters: 0
0 0 0 0 0 0
Link entry flow (veh); Mean initial queue (veh): number of vehicles queuing on the link at the start of the time slice; Vehicle arrivals (veh): number of vehicles in each class reaching the stopline on the link in the time slice; Departures from queue (veh): number of vehicles which leave the link in the time slice; Mean final queue (veh): number of vehicle queuing on the link at the end of the time slice; Spare throughput capacity (veh): difference between the maximum throughput capacity of the link and the number of vehicles which leave the link in the time slice; Mean PCU factor (Passenger Car Units); 14
0
0 0 0 0 0
= ratio of arrivals to capacity at stop line, to be used in place of degree of saturation; Mean total and queue time per vehicle (sets); Total delays by source (free moving, flow-delay or queuing) (veh.h); Percentage of occupancy; Measure of the number of stops, as a percentage of arrivals; An estimate of the efficiency of signal coordination.
Rho
The following information is necessary for signal controlled links: 0
0 0
Plan type Cycle time (sets) Green time (sets)
The following network totals for each time interval are printed out: 0
0 0 3.1.3.4
Total times by vehicle class (veh.h) Total distances travelled (veh.km) Total fuel consumption (litres) Link-by-Link All Intervals Tables
The following lists the tables that can be output for each time slice: Arrivals (veh/h) Capacities (veh/h) Mean final queues (veh) Mean queuing times per vehicle (set) Mean travel times per vehicle (set) Total delay (veh-hr) Average speed of a car (km/h) Generalized costs 3.1.3.5
Point to Point Speeds
This output indicates the variation with time of the average straight-line speed (km/h) for selected O-D movements. 15
3.1.3.6
Turning Movements: For Selected Intersections or For All Links
These data provide detailed information for all time slices of turning movements. The first form of this option is intersection-oriented and contains additional information on flows, signal timings, final queues, and mean queue time for the links feeding the intersection. The second form provides turning movements from all links without any additional information.
3.1.4 Demonstration [14] 3.1.4.1
Test Network
The test network shown on Figure 3.2 has been designed to demonstrate the use of the facilities in CONTRAM [14]. 3.1.4.2
Input Data Files
TEST.NET (Appendix A): Network and Time Data TEST.DEM (Appendix A): Traffic Demand Data TEST.CON (Appendix A): Control Data 3.1.4.3
Running the Program
The main executable program is called CONTRAM7.EXE. The command CONTRAM TEST is used to run the program with the TEST data files. 3.1.4.4
output
Three output files are created:
1) 2) 3)
TESTRES (Appendix A): Normal Results file (printer file) TEST.RTE (Appendix A): Vehicle Route file (detailed information for each packet path) TEST.PAF: Post Analysis Output File
- Demonstration of UFPASC (User Friendly Post Analysis System for Contram) Input:
TEST.PAF Post Analysis file TEST.RTE Vehicle Route file 16
FIGURE 3.2 CONTRAM TEST NETWORK
5005
5004
224 --:
1’ 5003
D
I
I
/
223
/
, ! \
i t 215
17
output:
OUTPUT1 (Appendix A)
- Demonstration of UFDESCS (User Friendly Data Entry System for Contram) Input: TEST.NET or TEST.DEM or TEST.CON - Demonstration of COMEST (Constrained O-D Matrix Estimation) Input:
X2.0BS Observed link counts X2.RTE Assigned routes and flows in CONTRAM type packet route format X2.CON Control file
output:
X2RES Results file
3.2 SATURN SATURN (Simulation and Assignment of Traffic to Urban Road Networks) is a computer model developed at the Institute for Transportation Studies, University of Leeds, for the analysis and evaluation of traffic management schemes over relatively localized networks (typically of the order of 100 to 150 intersections) [ 151. It is primarily intended to be used as a highly sophisticated traffic assignment model. This sophistication is due to a highly detailed simulation of delays at intersections. Unlike conventional assignment models, SATURN places great emphasis on intersections and specific turning movements as opposed to links.
3.2.1 Basic Structure The basic structure of SATURN incorporates two phases, as shown in Figure 3.3, a simulation and an assignment phase [ 151.
18
Figure 3.3 The Simulation and Assignment Phases of SATURN
NETWORK DATA ->
SIMULATION
NET LINK FLOWS
FLOW DELAY CURVES
ASSIGNMENT
19
Q l?x t/ 0 0 n Z Q I-CD
Z u-l
.
-
-
FIGURE 4.3 COMEST-CONTRAM RELATIONSHIP
.
2)
3)
a set of target link counts, which may be time-dependent and disaggregated by the three CONTRAM vehicle classes; a set of prior O-D movements and routes, in the form of a CONTRAM-type route file containing the routes and times of a number of packets.
4.3 RODIN [20]
RODIN is an external software developed by Nick Taylor (TRRL) and intended to be used in relation with CONTRAM to simulate route guidance. This program converts a packet route file output by CONTRAM into an O-D matrix and a set of routes which it embeds as fixed routes in a copy of the network file. The O-D movements are duplicated and each set is preceded by a percentage multiplying or split factor. When rerun using the new network and O-D files as data, the first set of 0-Ds is assigned on the fixed routes (i.e. along the original routes), while the second set is assigned to minimum cost routes in the usual way. This provides a framework in which experiments involving two user classes (guided and unguided vehicles) can be performed. The use of RODIN in relation with CONTRAM is highlighted in Figure 4.4. RODIN is designed to perform the basic operations described above. In addition to these functions, it provides: 0
0 0
a choice of methods for setting packet sizes; alternate vehicle class for the second set of 0-Ds; randomization of the output O-D counts.
36
FIGURE 4.4 RODIN-CONTRAM RELATIONSHIP
37
5.0
SMART CORRIDOR
Five key factors led to the decision whereby the Smart Corridor in Los Angeles, California would be used as the integrated freeway/arterial network in the CONTRAM simulation of the potential benefits of in-vehicle information systems.
1) 2)
3) 4)
5.1
The availability of a good database in terms of traffic counts, arterial geometric considerations, average arterial and freeway travel times, and freeway capacity calibrations. The size of the corridor and the fact that the corridor is experiencing traffic congestion and incidents occur regularly. The interest and continued assistance of CALTRANS and the City of Los Angeles Department of Transportation. The Pathfinder in-vehicle motorist information and road navigation project that is currently underway within the corridor.
Database
As mentioned previously, this project is a continuance of an earlier project [3]: the database used in the earlier project provided the vast majority of information used in setting up the CONTRAM model. Due to time and resource constraints, and since the earlier project had only evaluated the morning peak period, it was determined that the morning peak period would be used in all analyses. The morning peak period captures mostly work trips; therefore, people are typically more time conscious. Additionally, the morning peak period provides a more defined peak period as well as the fact that the arterials have more available capacity in the morning peak hours. Demand data was provided to the earlier research effort by the City of Los Angeles Department of Transportation and Caltrans.
5.1.1 Supply Parameters To properly code the network into the CONTRAM model it was important that the supply side of the Smart Corridor be coded properly. These supply parameters consist of the link distance, the cruise speed on each link, and the number of lanes and the ideal saturation flows per link for each intersection approach. The saturation flows used on each link were a result of the earlier research team’s effort to calibrate the model. Table 5.1 summarizes the general guidelines established for the saturation flows.
38
Table 5.1 Ideal Saturation Flows Ideal Saturation Flow Movement Type
(vPkPl)
Exclusive Through
I
1700
Exclusive Left (Protected)
I
1600
Exclusive Right
I
1450
Shared Through-Right
I
1700
The ideal saturation flow for a shared through-left movement was calculated by reducing the ideal saturation flow for an exclusive left turn movement by applying a left turn factor. This factor was determined from utilization of Chapter 9 of the 1985 Highway Capacity Manual[21]. An absolute minimum of 450 vphgpl was used as a result of the advice from the City of Los Angeles Department of Transportation. The ideal saturation flows for exclusive left-turn movements with permitted phasing was calculated based upon the relationship of the exclusive left permitted saturation flow rate versus the opposing flow rate. Once again, the saturation flows were determined from Chapter 9 of the 1985 Highway Capacity Manual [21]. As before for shared through-left movements, an absolute minimum of 450 vphgpl was used on advice of the City of Los Angeles Department of Transportation.
5.1.2 Control Parameters The control parameters required for the CONTRAM model consist of the signal timing data. Information such as interval lengths, minimum phase durations, cycle lengths, offsets/yield points, reference intervals, type of signal control, and phase sequencing were all obtained from the City of Los Angeles Department of Transportation.
5.2
Size of the Corridor and Traffic Congestion
The Santa Monica Freeway in Los Angeles is considered to be one of the most congested freeways in the world. As one of eight freeways which provides direct access to the downtown Los Angeles area, the
39
Santa Monica Freeway is also.the only facility which connects the west side of Los Angeles to the central downtown region. The Santa Monica Freeway is the only east-west freeway between the Santa Monica Mountains to the north and the Artesia Freeway to the south, a distance of approximately 13 miles. The five major arterials, Olympic, Pica, Venice, Washington and Adams Boulevards are connected to the freeway by approximately 15 major north-south streets. Figure 5.1 displays a map of the entire corridor. The principal’cause of traffic congestion is the peak hour(s) travel demands. Although the Santa Monica Freeway is four to five lanes in each direction, the travel demand during the peak hours still exceeds the amount of available freeway capacity. Daily traffic volumes on the freeway range from a low of approximately 180,000 to a high of nearly 315,000 close to the downtown area. Stop-and-go conditions exist daily during the peak hours on the freeway, where the average speeds throughout the corridor on the freeway are often below 35 miles per hour in both directions. Traffic on the parallel arterials is different from that on the freeway. Olympic Boulevard carries the most traffic of the five parallel arterials with a range of approximately 14,000 vehicles per day to nearly 32,000 vehicles per day near Century City. Adams Boulevard is the least travelled arterial with volumes ranging from 2,600 vehicles per day to nearly 11,000 vehicles per day. Adams, Washington and Venice Boulevards have significant amounts of unused capacity. Therefore, the arterials offer a considerable savings over the freeway in terms of travel time, especially when an incident occurs on the freeway. Thus, diverting freeway traffic to one of the major arterials in an incident scenario is a high priority of the Smart Corridor Demonstration Project.
5.4
Pathfinder
Pathfinder is an experimental project designed to test the feasibility of using the latest technological devices to assist motorists in avoiding traffic congestion. The Smart Corridor is the test bed for the project. The project provides drivers of specially equipped General Motors Oldsmobile Eighty-Eights, real-time information about accidents, congestion, highway construction, and alternate routes. The invehicle motorist information and road navigation system demonstration project is being sponsored by Caltrans, the Federal Highway Administration and General Motors. Since the objective of this research is to determine the potential benefits of in-vehicle information systems using the CONTRAM model, meaningful results may be used at some point to compare with those to come out of the Pathfinder demonstration project. Thus, any results found from this research may be compared with “real-world“ results to make a more definitive determination as to what the potential benefits may be since each project is using the Smart Corridor as its test bed.
40
FIGURE 5.1 LOCATION MAP - SMART CORRIDOR
0
Source:
C
E
A
N
California Department of Transportation State Highway Map
-_
41
6.0
INITIAL MODEL APPLICATION
The purpose of this chapter is to describe the initial applications of the CONTRAM model to the Smart Corridor with particular emphasis on freeway performance modelling. The freeway performance modelling was found to be more difficult than originally anticipated and required a number of modifications which are described in this chapter. The freeway performance modelling undertaken for a simple directional freeway is presented first, and then the modelling of the I-10 Santa Monica Freeway is discussed. The CONTRAM model was primarily developed for use in the design of traffic management schemes for urban signalized arterial networks. As mentioned in Chapter 4, CONTRAM has the ability to represent limited access and buffer network roads. The speed flow relationships represent the relationship between average speed and flow on roads where cruise time is a significant proportion of total time and journey times on links in a buffer network in order to simulate the general effects of capacity restraint. A standard COBA type speed/flow relationship is used whereby two linear sections of different slopes, one representing the break point speed/flow and the second representing the capacity point speed/flow are used. In order to gain a more clear understanding of the freeway modelling characteristics within CONTRAM, a linear test segment of freeway was designed. To evaluate the characteristics of the CONTRAM model, both manual calculations and the FREQ model were chosen as tools for calibration. The FREQ family of freeway simulation models has been in existence since the 1960’s [22]. Both manual calculations and the FREQ model were used in the I-10 calibration process. FREQ is a macroscopic deterministic simulation model in which time can be broken into equal discrete time-slices and the directional freeway segment divided into homogeneous subsets with demands and capacities remaining constant during each time slice . Merging and weaving analysis, when selected, follows the 1965 Highway Capacity Manual procedures. A limitation to the FREQ model is that freeway congestion can only begin and end at boundaries between time slices. The queue contour maps in FREQ provide a picture of both bottleneck locations and queue lengths over both time and space. The speed contour maps also provide a picture of speeds in the queue in the bottleneck and for every subsection over time and space. The criteria for freeway calibration were based on three key considerations. The first consideration was that CONTRAM identified the bottlenecks in the same subsections as those determined by manual calculations and shown in FREQ. The second consideration was that of queue length. Once the bottlenecks were identified and located properly, queue lengths were evaluated to see whether CONTRAM had the proper queue lengths as shown in the manual calculations and determined by FREQ. The third consideration in the freeway calibration process was that of freeway speeds in terms of free flow speeds, speeds at the bottleneck, and speeds within the congestion. 42
6.1
Linear Test Freeway Segment
In an attempt to gain a more comprehensive understanding of the operation of the CONTRAM model as it relates to freeway operations, a simple linear test freeway segment was created as shown in Figure 6.1. The test freeway segment is 8,100 feet long with five subsections and nine time slices. Figure 6.1 also presents the ‘time slice demands as well as the capacities assumed for each subsection. In the test segment, the first subsection through the third subsection is composed of three lanes. The fourth subsection is two lanes with a length of 100 feet. The fifth and last sub-section is composed of three lanes and is 2000 feet long. The capacity of each lane of the freeway is assumed to be 2000 passenger vehicles per hour. The demands were set to create queuing at the bottleneck in the fourth time slice. All queuing would dissipate by the beginning of the ninth time slice. A manual shock wave analysis was conducted to predict queue lengths and speeds in each subsection during each time slice. The complete results of the analysis are contained in Appendix D. Figure 6.2 displays the shock wave and speed information in km/hr by time slice and subsection. In Figure 6.2 the shock wave can be seen beginning in subsection three at the beginning of time slice four. At the end of time slice six and beginning of time slice seven, the queue reaches its longest at approximately 700 meters. The shock wave ends during time slice eight. Figure 6.3 presents the speeds and shock wave as predicted by CONTRAM. As seen in the figure, the speeds predicted by CONTRAM do not match those from the manual calculations. The queue pattern does not identically match that of the manual calculations either. Several key reasons for the differences between the manual analysis and the CONTRAM output deserve mention. It should be noted that because the CONTRAM model is macroscopic and sends vehicles through the system in packets, not all packets make it through each subsection during each time slice. To identify the bottleneck in the proper subsection, the approach used was to code the saturation flow of a link as the capacity of the downstream link. This technique is theoretically correct since the saturation flow of each link is measured as the throughput capacity of that link. Thus, in this particular application the bottleneck was properly identified in sub-section four. The key input to calibrate queue lengths is the minimum distance headway. Since the CONTRAM model is designed for arterials, an estimated storage capacity is calculated by the program based on a minimum distance headway that is either provided by the program or input by the user. The default provided within the model for the minimum headway distance is 5.75 meters. The minimum distance headway directly determines the densities that are represented on the freeway. Since freeway densities are much lower than those at an intersection, the minimum distance headway in the CONTRAM model must be 43
-_ 1... .
FIGURE 6.1 LINEAR TEST FRJ%WAY INFORMATION
I
I
3800
3800
3800
t
44
I
I
3800
3800
FIGURE 6.2 SPEED/SHOCK WAVE INFORMATION - MANUAL CALC.
Time Slice
ss 1
ss 2
ss 3
- ss 4
ss 5
1
SPEED
SPEED 94
SPElED 94
SPEED
SPEZD -9-i
2
SPEEJD
SPEED
SPEE3l
SPEED
9-L
4
B3
93
93
3
SPEED
SPEED
SPEEO
91
91
91
4
SPEED
SPEED
91
91
5
SPEED
SPEED
91
91
6
SPEED
SPEED
Bl
91
7
SPEED
8 9
-- --
93
SPEED
SPEED 93
93
SPEED
SPEED. 93
.
93
SPEED 68
SPEED 58
56
.
SPEED
70
SPm
SPEED A
33
93
spIEzzD /I1
91
S-PEED 91
;4
SEED 91
SPITED 91
SPEED . 55
SPEZD 91 .
SPEED 58
SPEED
SPEED
SPEED
91
93
SPEED 93
-_
45
SPEED 88
SPUD 93
FIGURE 63 SPEED/SHOCK WAVE INFORMATION - CONTRAM
Time Slice
ss 1
ss 2
ss 3
- ss 4
ss 5
1
SPEED 4s
SPEED
SPEED
SPEED
10%
41.
SPEED ‘95-
2
SPEED 45
SPEED
45-
SPEED 10s
SPEED 9
SPEED 75
3
SPEED 4s
SPEED 95
SPEED lo$
SPEED 56
SPEED 95
4
SPEED
5
SPEED 15
6
SPEED 45
7
SPEED qs
45
4
, SPEED Lv 95
45
8
SPEED 95
9
SPEED 45
SPEED ?g
I
SPEED s5 .
SPEED, 4s
SPEED IOS
-- _ - -..
46
~- -
SPEED 45
SPErn CL
SPEED 95
SPEED GiO
SPEED qs
manipulated to create densities that more closely reflect those that are observed on freeway segments. The storage capacity of each link determines the amount of queuing each sub section can handle. Table 6.1 illustrates the conversion of distance headway in meters to densities in vehicles per mile per lane. The minimum distance headway input by the user is universal over all links, thus densities cannot be changed at each link. Through a series of tests for the specific linear freeway segment under investigation in this study, the minimum distance headway of 20 meters was determined to most effectively represent queue lengths as found in the manual calculations.
Table 6.1 Relationship Between Minimum Distance Headway and Density
10.00
161
12.50
129
15.00
107
I
16.00
101
17.00
95
18.00
89
I
19.00
85
20.00
80
To more realistically represent speeds on the linear test segment as determined in the manual calculations, the speed flow relationship curve as specified in CONTRAM was modified in conjunction with the input cruise speeds. The most effective method of replicating speeds determined by the manual calculations was to use the speed-flow relationships only at the bottleneck and one subsection downstream from the bottleneck. As mentioned previously, CONTRAM has the ability to model high speed limited access roads through the use of a speed-flow curve. Figures 6.4 and 6.5 present the speed flow curves that were 47
used to best replicate the results as found in the manual analysis for the bottleneck and downstream section. The speed flow curve is a replication of the COBA curve used in the CONTRAM model as described in Chapter 4. As shown in the figures, V, represents the point at which the highest level of flow is observed. Vb represents the break point speed and V, represents the speed at which the speedflow relationship predicts an inter-vehicle headway equal to the minimum distance headway as input from the user. The curves shown in Figures 6.4 and 6.5 are those that provided the best results in terms of matching the ‘queue lengths and speeds as derived from the manual calculations. In the subsections without the speed flow relationships the cruise speed of 92 km/hr was used. The free flow speeds on each link do not match because the model truncates the mean time per vehicle in seconds on the link and does not represent a constant free flow speed. The speed flow relationship that CONTRAM uses for modelling high speed limited access roads does not allow for modelling traffic congestion. That is, the speed-flow relationship only obeys the upper limb of the true speed flow relationship on a freeway. Thus, the speeds as output from CONTRAM do not obey the speed-flow relationship as input to the program in congestion. The method CONTRAM uses for calculating free flow speeds does not provide for a constant speed across all free flowing links. Additionally, for congested links, the speeds as represented by the model are much too high. This has been identified as a problem and is something that needs to be addressed in future research efforts if CONTRAM is to be used to represent a freeway segment. Although freeway conditions could be represented somewhat realistically through the use of the procedures described in the previous paragraphs, a question regarding the influence of freeway ramps on bottlenecks and other sections of the freeway remained unanswered. To help answer some of the remaining questions regarding the operation of the model another linear test freeway segment was introduced. However, this time a real life freeway corridor was used. The Santa Monica Freeway in Los Angeles, California was modelled in the eastbound direction to reach a better understanding of the operations of the CONTRAM model. For comparison purposes, the FREQ model was also applied.
6.2
Smart Corridor Freeway Modelling
The eastbound section of the twelve mile Santa Monica Freeway was the next segment used in the freeway calibration process. The network consists of 32 subsections with 16 on-ramps and 15 off-ramps. The demand data consists of 17 origins and 16 destinations over eight 30 minute time slices. The network and demand information is shown in Figures 6.6 and 6.7. The elements that were given special consideration in this process were; bottleneck location, queue length, and average speeds.
50
6.2.1
Bottleneck Location ,and Queue Length Pattern
Table 6.2 highlights the main freeway events as predicted by FREQ. Table 6.2 FREQ Main Freeway Events
Time
Bottleneck Location
Queue Length (miles)
7:30 - 8:OO
ss 29
8.0
8:00 - 8:30
s s 29
3.6
8:00 - 8:30 1
ss 14
I
2.2
9:oo - 9:30 1
ss 14
I
0.3
As seen in the table, the major bottlenecks were identified in subsections 14 and 29. Each freeway subsection was coded as an uncontrolled link in the CONTRAM network while the on-ramps were coded as signalized links with 100 percent green time. The first approach was to code the saturation flow of a link as the capacity of the downstream link. This technique is theoretically correct since the saturation flow of each link is measured as the throughput capacity of that link. The results obtained from this technique did not closely resemble the results provided by FREQ. A possible explanation for the discrepancies could be the ramp merge and diverge points and their influence on capacity. The second approach was to input the capacity of each subsection from FREQ as the saturation flow. This technique led to the results shown in Table 6.3. As shown in the figure, the bottlenecks were usually identified one subsection downstream from those in the FREQ runs.
Table 6.3 CONTRAM Main Freeway Events
Time
Bottleneck Location
Queue Length (miles)
7:oo - 7:30
ss 30
1.4
8:OO - 8:30 1
ss 30
8:00 - 8:30
s s 22
9:oo - 9:30 1
ss 14
I
1.6 1.0
I
0.2
The comparison between Tables 6.2 and 6.3 shows that CONTRAM typically identifies bottlenecks one subsection downstream from those of FREQ, and there does not seem to be a way of modifying CONTRAM to model it correctly. The next objective was to obtain the queue patterns as produced by FREQ. Queue length patterns are directly affected by the storage capacities input in the CONTRAM network file. As mentioned previously, the minimum distance headway in CONTRAM determines the optimum densities that will be replicated on each link. For this particular application, the minimum distance headway which provided the queue pattern which most closely resembled those provided by FREQ was found to be 50 meters or a density of 32 vehicles per mile per lane. Figure 6.8 shows the queuing patterns from both FREQ and CONTRAM. While not perfect, this was the closest agreement based on modifying the minimum distance headway.
54
FIGURE 6.8 QUEUING PA’ITERN - FREQ, CONTRAM COMPARISON
. s . 7 .
5 . . : . L 7 . :: , * c . I .
CONTRAM Queue Length Contour Map
- 6:OO I 1
1
i iJ4
I 5
I 6
3
/ II ; 9'1'1 13
I
'1'S
Subsection
55
III 18 19
Ill 22
L
i4
Ii 26
;a
I 8 31'
6.2.2 Average Speeds
Figure 6.9 shows the speeds predicted by FREQ. When calibrating the speeds predicted by CONTRAM, two key elements must be considered: free-flow speeds and speed-flow relationships. A uniform freeway free-flow speed of 85 km/h (53 mph) was adopted. Based on the conclusions of Section 6.1 and trial and error, speed-flow relationships were only used in the bottleneck subsections (14, 22 and 30) after the bottleneck locations were identified by a first run. As seen in Figure 6.9, once again CONTRAM predicts speeds within the congestion that are much too high. The speeds one subsection downstream from the bottleneck are too high. It is unclear just how much influence the freeway on-and off-ramps have on the speeds as determined by the model. Further analysis is required to determine the precise amount of influence on-and off-ramps have on average speeds. 6.2.3 Overall Summary Measures
Table 6.4 displays the overall network wide summary results from the FREQ run and the CONTRAM run. Table 6.4 Overall Network Wide Summary Results Comparison
Network Summary Measure
FEQ
CONTRAM
Total Travel Time (veh-hr)
8035
7546
Total Travel Distance (veh-mi)
290975
306388
Overall Network Speed (mi/hr)
36.2
40.6
As seen in the table, from a system-wide perspective, the results are fairly comparable. Once again, the speed is higher in the CONTRAM model which is consistent with the previous freeway modelling effort.
56
FIGURE 6.9 SPEED PATTERN - FREQ, CONTRAM COMPARISON
TIC: .-a :“il:E
. . . . . . ..s.....*
..,.......
*..........
. . . . . . . . . . ..~-.-.-....-.-.........................................
..
CONl-FwJu contour Map Speed Tme Slice 5 5 5 6 5 5 5 5 5 5 6 5 55555SS56SS5555555 1s 5 2 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 55555S666S5S5555~ 6 5 5 6 3 4 3 5 5 5 5 5 5 5 5 6 5 5 5 3 3 5 3 5 4 6 6 4 5 1 3 5 5 5 5 6 5 4 4 5 5 5 5 5 5 5 5 5 5 5 4 2 3 4 3 5 4 4 4 4 3 3 1 3 5 6 6 5 5 5 5 6 5 4 4 4 4 5 4.2 12 3 4 4 411 3 4 1 3 4 2 2 1 6 5 5 ~~~~~~544446623565541, 3 6 2 4 6 2 2 , 3 5 5 7 5 5 5 5 6 6 6 4 4 5 5 5 3 3 6 5 5 5 6 6 3 5 6 6 5 6 4 4 , 5 6 5 5 5 5 8 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 5 5 5 5 5 5 5 sub,
2
3
4
5
5
7
8
9
10
11
12
13
14 1s 16
17
t8 19 27, 2, Z? n
The digit levels denote the first dlgit of the Operating speed (ex 4 means 4049 mph)
57
24
X 26
27
28 29 30 3 fz
..r,:r: c:,: :.t i,..,:I::
6.2.4 Conclusions The freeway calibration process focused on three major elements of freeway operations. The first element consisted of identifying freeway bottlenecks in the appropriate subsection. It was determined that for a simple network with no on-and off-ramps to identify the bottleneck in the proper subsection, it was necessary to code the saturation flow of a link as the capacity of the downstream link. This technique is theoretically correct since the saturation flow of each link is measured as the throughput capacity of that link. However, for a freeway section with a number of on-and off-ramps this technique did not prove effective and the bottlenecks were identified one subsection downstream. 2)
Once the bottlenecks were identified and located properly, queue lengths were evaluated to see whether or not CONTRAM had comparable queue lengths to those estimated by the manual calculations and determined by FREQ. The key input to calibrate queue lengths is the minimum distance headway. Since the CONTRAM model is designed for arterials, an estimated storage capacity is calculated by the program based on a minimum distance headway that is either provided by the program or input by the user. The default provided within the model for the minimum headway distance is 5.75 meters. The minimum distance headway directly determines the densities which in turn determine the storage capacity of each link. The storage capacity of each link directly influences the queue lengths found throughout the network. For the first linear test segment a value of 20 meters was used, while in the eastbound Santa Monica Test Segment, a value of 50 meters was used.
3)
The third consideration in the freeway calibration process was that of freeway speeds. This is the area of most concern. In free flow conditions, the freeway speeds could be approximated through the use of the speed-flow curve and also as input by the cruise speed. However, for congested conditions, the speeds represented by the CONTRAM model are not realistic. The speeds within the queues are much too high and the speeds at the bottleneck can only be approximated at best through the use of the speed flow relationship.
58
7.0
DESIGN OF THE EXPERIMENT, REFERENCE BASE ASSIGNMENT, SIMULATION AND VALIDATION
This chapter describes the design of experiment as well as the steps undertaken to develop a reference base assignment. The first section outlines the experiment while the remainder of the chapter is devoted to the development of the base reference assignment.
7.1
Design of Experiment
After several months of attempting to model freeway congestion in a realistic fashion, it was decided that despite the limitations of the model with respect to freeway congestion, attention would be given to modelling the Smart Corridor with the CONTRAM model. With the goal of determination of the benefits of in-vehicle information systems, the experiment was designed as shown in Figure 7.1. The first step was to develop a reference base assignment and simulation that as closely as possible represented the Santa Monica Smart Corridor. The remainder of this chapter is devoted to discussion of this process. Since, from the previous study [3], the benefits for non-incident conditions were found to be relatively small, the next step was to model a freeway incident. Two incidents were created and are discussed in Chapter 8. Once the incidents were created, tests were begun to evaluate the benefits of in-vehicle information systems. Investigations were made with varying percentages of equipped vehicles from 0 to 100 percent. Chapter 9 describes the results of the experiments with varying percentages of equipped vehicles.
7.2
Network Description and Calibration
Figure 7.2 presents the network modelled and used in the analysis. Approximately nine miles of the SMART Corridor with two parallel arterials were coded into the model. The eastern boundary of the network is the Harbor Freeway, while the western boundary is LaCienega Boulevard. In addition to the Santa Monica Freeway, the two parallel arterials coded were Washington Boulevard and Adams Boulevard. Ten major north-south streets connecting Washington, Adams, and the Santa Monica Freeway were coded as well. As previously mentioned, the previous project (PATH-ITS-UCB-PRR-88-2) provided a comprehensive data base for this analysis, in the form of FREQ and TRANSYT input files. All data provided by the previous project represents the year 1987. Some updated information regarding speed-flow relationships provided by Caltrans was used as well.
59
FIGURE 7.1 DESIGN OF EXPERIMENT
and Simulation
Severe Incident
Percentage of Vehicles Equipped with In-Vehicle
60
Information
FIGURE 7.2 MAP OF NETWORK MODELLED N t
Crenshaw
LaErea
61
The coded network is composed of approximately 200 arterial links. A uniform free-flow speed of 35 miles per hour was adopted for the arterials. The link throughput capacity was determined by the rules described in Section 5.1-l. The signal timing data were taken from the earlier study as well. Some minor modifications were made in the signal timings, especially at freeway ramp intersections, to improve the simulation of the network. The freeway part of the corridor is composed of 51 uncontrolled links. The westbound direction is represented by 23 uncontrolled links, while the eastbound direction is made up of 26 uncontrolled links. Two additional links were used to represent the Harbor Freeway at the eastern boundary of the system. A uniform free-flow speed of 60 miles per hour was used on all freeway links. On the basis of the experiments described in Chapter 6, no speed-flow relationships were used and each link throughput capacity was input as the corresponding FREQ capacity. The storage capacity was determined by the standard default formula using the minimum distance headway of 20 meters which was found to be optimum after calibration as described in Chapter 6. The freeway network includes 24 on-ramps (11 for the westbound direction and 13 for the eastbound direction). These links were coded in CONTRAM as signalized links with 100 percent green time. Thus, ramp metering was not modelled as a part of this project. A uniform free-flow speed of 30 miles per hour was used on freeway ramps.
7.3
Corridor Demand
To achieve a realistic demand level and pattern, a three step process was undertaken. The three steps consisted of the following:
1)
creation of an origin-destination matrix;
2)
using the COMEST program, the origin-destination estimator described in Chapter 4;
3)
manipulation of the COMEST output to create a more realistic demand level
The third step, manipulation of the COMEST output, was necessary because of the crude nature of the original origin-destination matrix which was created in step one. The following paragraphs outline the procedures taken to reach a final origin-destination matrix with demand levels similar to those used in the previous study of the SMART Corridor.
62
7.3.1
Initial Origin-Destination Matrix Generation
As mentioned in Chapter 4, CONTRAM requires the user to input an origin-destination matrix. For this particular application there was not detailed enough origin-destination information available as mentioned in Chapter 5. Although the City of Los Angeles did provide origin-destination information for the vicinity in and around the SMART Corridor, due to the coarse nature of the data and the time and resource considerations, it was decided that the best option was to create a fictitious origin-destination matrix based on traffic counts provided in the previous study and then apply the COMEST program to reach a realistic representation of the demand throughout the corridor. The first step in creating the fictitious origin-destination matrix was to determine where origins and destinations were to be located. The decision was made to create external origins and destinations as shown in Figure 7.3. This decision was made primarily because information regarding the total number of vehicles entering and exiting each newly created external origin-destination link was readily available for the time period from 790 a.m. to 8:00 a.m. No internal origins and destinations were created since the primary sinks and sources within the corridor were not well understood, and they were considered to be less important than the external sinks and sources. Once the locations of the origins and destinations were decided, the total number of vehicles entering and exiting each link for the time period from 7:00 a.m. to 8:00 a.m. were entered into a spreadsheet as shown in Figure 7.4. Once the row and column sums of the matrix were fixed, the next step was to balance the entries in the matrix. This step was done mostly by trial and error with the assistance of the graph shown in Figure 7.5. The objective of the fictitious origin-destination matrix was to help lead the COMEST program in the proper direction through the use of the observed link counts. A final fictitious matrix was chosen and a CONTRAM run was made. The CONTRAM run provided the COMEST program with the necessary packet route file and an original origin destination matrix from which to work.
7.3.2
Use of the COMEST Program
The second step in the corridor demand analysis was to create an “observed traffic count“ file from the data provided in the Al-Deek, Martello, Sanders and May study [3] on TRANSYT and FREQ runs for the time period from 790 a.m. to 890 a.m. For the initial application of COMEST every link in the network was input to the observed traffic count file. After many iterations of COMEST and CONTRAM it was discovered that there were too many links for COMEST to balance the traffic counts. The demand
63
FIGURE73
Location of Origins and Destinations
Legend -7T--- Origin/Destinalion
64
I
FIGURE 7.4
t
t’
Origin/Destination Matrix
4
0
5 6 7 8 9 10 Ii 12 13
m Gi
0 0 0 0 0 0
: 0 0 0
14 , 15 16 17 is 19 20 21 22 23 11 I 24 11 25 II I I t XII I I -- , sull 1 01 UJ ObsTda( BMO Il3% )
I
0 0 0 0 0 0 0 I
I I I 01 741 1
I
I
I
I
I
I
I
I I I I I I I I I I I I I I O( 01 01 01 01 01 01 0 696 I1893 1164-r 11366 11269 11354 113% II307
I
I I
I I I 01 01 01 01 2300 120X I6716 1 i243 1
01 248 1
I 01 631 1
I 0 1 1
I I 0 607
0 lW6 1
01 727 1
I 01 761 1
0 0 0 I 01 u]I [I 5 % 11664 I1966
I 01 1 0 Ul of lM8 11349 1
0
4aJ
0 0 0 0 0 0 0 0 0
1694 1437 972 1214 1084 1450 1434 35m ICKU
0 0 0 0 0 0 0 0 0 0 0 0 0 0
6613 11% 94 1546 329 1231 1193 11% 104
a
2lii
I%7 1414 1046' 43970 Tdd 43970 T&d 0 on.
FIGURE 7.5
Origin-Destination Difference 250 200 150
0 2 E aI
50
i5
+-j
I
0 C
I
-100 -150 -200 -250
I
1
I
2
3
I
Ill
5 4
II
I
7 6
I
9 8
I
I
11 10
11
I
13 12
I
15 14
I
I
16
I
18
Origin/Destination 0 Dest Act-Total Diff m Org Act-Total Dii
66
I
19
17
I
i
21 20
II
23 22
I,
25 24
26
pattern created by COMEST. was not very realistic due to the coarse nature of the original “fictitious” origin-destination information. To achieve a demand pattern representative of those provided by reference [3], the best results were provided by COMEST when the only observed link counts input were those counts on the eastbound freeway and the corresponding ramp junctions. The relationship between CONTRAM and COMEST as explained in ‘Chapter 4 was next used to generate a final origin-destination matrix. Four CONTRAMCOMEST iterations were conducted to obtain the final demand pattern for the 7:00 a.m. to 8:00 a.m. time period.
7.3.3 Final Origin-Destination Matrix Once the demand pattern provided by the COMEST runs was satisfactory for the 7:00 a.m. to 8:00 a.m. time period, the next step in the process was to develop origin-destination information for the eight 30minute time slices from 6:00 a.m. to 10:00 a.m. Based on information provided by the previous study [3] and information regarding the performance of the system, the demand was manipulated to create an origin-destination matrix for the eight time slices. Table 7.1 displays the time slices and corresponding level of demand factors. Table 7.1 Corresponding Time Slice Demands
Time Slice
Demand Factor
6:00 - 6:30
30 %
6:30 - 7:00
80 %
7:oo - 7:30
100 %
7:30 - 8:00
100 %
8:00 - 8:30
80 %
8:30 - 9:00
60 %
9:oo - 9:30
40 %
9:30 - lo:oo
30 %
67
Once the demand factors were applied, a final origin-destination matrix was completed. The resulting sums from each origin and destination per time slice are shown in Figure 7.6. This matrix was then used as the demand information for the base CONTRAM run.
7.4
Base’Run Validation
Three key areas were examined to validate the model’s representation of the Smart Corridor. The first area was that of route choice and travel times/speeds. The second area of concern was that of bottleneck location on the freeway and the corresponding queuing pattern. The final validation process was in the overall network statistics. The following paragraphs describe the three major steps taken in the base run reference assignment validation.
7.4.1 Travel Times/Route Choice A critical element to the successful modelling of the Smart Corridor is to achieve an equilibrium assignment whereby the travel times via different routes between various sets of origins and destinations are not significantly different. If the travel times differ by a large magnitude then not much diversion will be seen even with a very severe incident. From the work described in Chapter 6, it was recognized that the freeway travel times were slightly lower than real life due to the fact that the speeds within the congested portions of the network are too high. With that in mind, travel time comparisons were made between a route on each parallel arterial only and a route on the freeway only. Figure 7.7 shows the travel time comparisons from Origin 1 to Destination 14, which is a freeway Origin to a freeway Destination. The travel times on the parallel arterials (Adams and Washington) include times on the freeway at the beginning and end of the route as well as the times taken to enter and exit the freeway. Thus, a second comparison was drawn and is shown in Figure 7.8. This comparison is made from freeway link 11 to link 29, which represents the length of Adams and Washington from Fairfax to Hoover. As seen in the figure, travel times on the arterial are much closer to that of the freeway in the heaviest demand time slices. As the demand decreases the difference in travel times increases. The second key element of travel time/speed is that of route choice. This was done primarily through the use of UFPASC (User Friendly Post Analysis System for CONTRAM) program. The UFPASC is an interactive program for examining the outputs from CONTRAM runs. The UFPASC produces tabular and graphical outputs for selected parameters from the results file produced by CONTRAM. UFPASC uses a menu system to set up the analysis stages for producing the selected outputs. UFPASC allows selective investigations of the results file produced by CONTRAM. 68
FIGURE 7.6 FIN& DEMAND INFORMATION ~T,,,AL “E”,CLE FI.OW R A T E S FRCH E A C H ORIGIN-CiidING ?ACH TIME SLICE (*H/H) ORIGINS
FLOVS
5001
2721
7228
9000
9000
7728
5390
3610
mj
0
5002
56
146
185
185
146
113
74
56
0
5003
629
1657
2073
2073
1657
1247
ali3
629
0
5004
125
335
424
424
335
252
171
12s
u
500s
288
771
954
954
771
578
385
2e.8
0
5006 5007
540 40
1433 128
1782 15-a
1782 isa
1433 128
la69 97
717 60
540 48
0
5008
305
a02
1004
1004
802
594
4n
305
0
5009
159
420
519
519
420
311
207
159
0
5010
309
811
1013
1013
all
602
404
509
0
5011 5012
7 766
23 2039
26 2542
26 2542
23
.14
9
7
0
2039
IS23
1019
766
0
5013
766
2039
2542
2542
2039
1523
1019
766
0
5014
la01
4831
6016
6016
4831
3563
,241o
jlug
0
5015
21
68
79
79
63
51
26
21
0
5016
25
72
88
88
n
53
33
25
0
5017
5
14
15
15
14
8
5
5
0
5018
95
267
329
329
267
196
iza
'* 95
0
5019
58
151
192
192
151
112
i-3
57
0
5020
103
272
336
336
2R
200
132
103
a
5021 . 5022
199
519
651
651
519
3a5
261
199
0
29
79
99
99
79
61
39
29
a
5023
338
933
1153
1153
933
696
462
350
a
5024
319
a50
1060
1060
bS0
632
424
319
a
so25
343
903
1128
ltza
903
674
44
343
0
5026
196
524
658
653
524
393
261
196
0
OTOTAL VEHICLE FLOP R A T E S D I R E C T E D T O W A R D S EACH DESTlHATlCNS
DESllNA~lON
DURING EACH TINE SLICE (WI/H)
FLGUS
9001
2984
7927
9075
9875
7927
5913
3951
2984
0
9014
1777
4715
5874
5874
471s
3523
2350
17i7
0
9013
523
1387
1727
1727
13a7
1030
_ 693
523
a
9012
1143
3038
3791
3791
3038
22n
1522
1143
0
9017
132
355
450
45Q
355
267
In
132
0
9018
69
la4
224
224
185
138
M
69
0
9019
37
108
128
123
loa
74
47
37
a
9020
46
140
172
172
140
106
64
4.5
0
9021
98
256
321
321
256
la9
130
SE
0
9022
18
51
60
60
51
32
2s
18
0
9023
206
578
686
724
578
406
291
zab
0
9024
121
314
394
39:
314
237
156
121
0
9025
45
130
159
159
130
97
59
4s
0
9015
148
390
490
490
3w
293
200
14E
a
9011
174
45a
575
575
458
346
234
174
0
9010
553
1471
la30
la30
1471
1102
738
553
0
9009
251
656
818
ala
656
490
327
251
0
9008
a2
223
274
274
223
163
108
12
0
9007
230
615
772
772
615
462
310
230
a
.
9006
77
216
265
265
216
161
103
77
0
9005
314
624
ioza
1028
a24
615
41s
314
0
9004
419
1112
1380
13&O
1112
a27
550
4Ia
0
9003
327
aao
1095
1095
F&o
659
439
333
0
9002m‘
401
1068
1332
1332
1068
801
535
407
0
9026
43
125
154
154
12.5
90
59
43
0
9016
33
94
114
114
9c
66
45
33
0
13620
'IO262
0
0707~~ YEH~CLE FL@J RATES EHlERlNG T H E N E T W O R K DURING E A C H TIM - -_
0
10251
27315
34026
34026
SLICE 27315
69
(VEH/H) 20359
FIGURE 7.7
Travel Times from Org. 1 to Dest. 14 Base Run 20.00
16.00
1200
10.00
6.00
I
2
5
4
6
Time Slice
++ Washington
70
7
8
FIGURE 7.8
Equal Distance Travel Times Base Run
1
2
3
4
5
6
Time Slice
+++ Washington
-t- Freeway Only -+- Adams
71
7
8
Thus, by using UFPASC a number of origin destination pairs were chosen and evaluated. An example of such an output is shown in Figure 7.9. As seen in the figure, the route chosen by most is that of the freeway. The average speeds and travel times shown in the figure represent averages over all time slices. The routes chosen from origin to destination were also examined to make sure they were reasonable.
7.4.2 Bottleneck Location/Queuing Pattern A key validation measure was that of freeway bottleneck location and queue length. Bottlenecks were identified in subsections 14 and 29 which match the work reported in Chapter 6. While the bottlenecks were identified in the same subsections as before, the queuing pattern was not exactly the same. Since the demand pattern is much more complex, it was not possible to achieve the same queuing pattern as before. However, the pattern as shown in Figure 7.10 does closely match that of the work described in Chapter 6. The main difference between the previous freeway-only work and the corridor base simulation run is that the queues do not back up as far from subsection 29 as before. In the freeway-only work, the queues from subsections 14 and 29 collided. In the base reference assignment, the queuing is not as severe as that described in Chapter 6 where the freeway only is modelled. However, because the bottlenecks were properly identified and the demand patterns were reasonable it was felt that existing queuing pattern shown in Figure 7.10 was acceptable to continue with the experiment. Therefore, it was concluded that comparisons made to the base reference assignments would be acceptable for analysis.
7.4.3
Overall Corridor Wide Summary Information
From a system-wide perspective, the amount of free moving delay compared to the amount of flow delay and delay caused by queuing was examined for its reasonableness. The total distance travelled, the overall network speed, and the total final queues were all examined for reasonableness. Table 7.2 presents the overall network summary information for the base reference corridor assignment. As seen in the table, the overall network speed is approximately 30 miles per hour which is in a reasonable range. The total freemoving time as compared to the delay due to queuing is also in a reasonable range. Appendix E contains a condensed input and output of the base reference assignment.
72
FIGURE 7.9 UFPASC EXAMPLE OUTPUT RWTE INFORMATION ttt* Origin 5001 and Destination 9015
TABLE Of ROUTES Route Links on Route ----> No. 1
7
-->
17 27
--> 2
1205
1505 17
-->
8 18
905 4
7
--> -2505
12
13
22
23
14 24
15 25
16 26
--, -->
205 10
11
12
13
14 2205
23
22
21
20
15 2101
16
--> -->
2009
205
7
-->
11 21
909 9
19
905
3
10 20
1001 23
18
17
9 19
7
--> -->
8 18
9 19
205 5305 2005
10
11
20
4905 1505
21
4801 905 -
12
13
2705
4701
2601
14 2501
15 2005
16 1505
--> -->
4603
7605
4105
3505
3005
-->
4601
7605
4105
3505
3005
-->
205
--> 5
7
--,
8
9 1505
2005
2505
10 905
4805 205
-->
TABLE OF FLOUS (Vehicles) Time Intervals
Route Veh. Type
NO.
12
TOTAL RUJTE OVERALL OVERALL 3
4
5
6
7
0
9
10
11
I2
13
fLOW
DIST. AVE.JCU. AVE.
(VEH) 22
0
0
0
0
0
308 10532
0
6.46
0
0
0
0
0
29 13390
0
0
836
57.6
0
0
0
0
a
8 12226
0
0
50.4
0
0
846
0
0
0
14 11951
0
0
0
0
1010
39.6
0
0
0
15 12169
80
50.4
22
0
0
0
0
0
,22 0
59 0
74 0
38
19
14
15
3
c
0
0
0
8
0
4
C
0
0
0
14
0
0
5
c
0
0
0
0
15
0
22
59
74
74
49
44
30
TOTALS 0
0
0
SPEED
0
c c
O,
TIHE
30
1 2
4.G 0
WI
0
--
73
57.6
FIGURE 7.10 QUEUING PA’ITERN - REFERENCE BASE ASSIGNMENT
1’
LINK-BY-LINK
ALL-TIME-SLICES
- HEAN FINAL WEIJES (WI)
RUH ON ll/ b/91
SHART CORRIDOR BASE RUN NETWORK and TIM DATA (6/11/91) SHART CORRIDOR BASE DEMAND COHTROL DATA SHART CORRIDOR ITERATIOH TTHE SLICES : 1 2
LINK
3
4
5
6
7
8
NUHEER
9
NO.&
TOTAL
TYPE
FINAL PUEUES 630
600
700
730
800
830
900
930
1000
1300
N
.O
.O
.O
.O
-0
.O
.O
.O
.O
.O
au
.O
.O
68.0
334.0
.O
.O
-0
.O
.O
402.0 201.0
9u
-0
.O
87.OF
94.OF
20.0
.O
.O
.O
.O
1ou
.O
.O
46.OF
44.OF
74.OF
.O
.O
.O
.O
164.0
11u
.a
.a
llO.OF
122.OF
126.OF
.O
.O
.O
.O
358.0
1.X
.O
.O
122.OF
137.OF
156.OF
.O
.O
.O
.O
415.0
13u
.O
.O
275.OF
257.OF
25l.OF
.O
.O
.O
.O
783.0
14U
.O
.O
37.OF
76.OF
39.OF
.O
.O
.O
.O
152.0
15u
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
-0
.O
.O
-0
.O
.O
.O
16U
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
1N
.O
.O
lau
.O
.O
-0
.a
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
105.0
199.OF
.O
.O
-0
.O
342.0
112.OF
.O
.O
.O
.O
232.0
19u
.O
.O
-0
.a
.O
2ou
.O
.O
.O
.O
.O
2lU
.O
.O
53.0
.O
52.0
22u
.O
.O
.O
143.0 12O.OF
.O
.O .o
.o
.O
23U
.O
.O
.O
24u
.O
.a
14.0
121.OF
14O.OF
.O
-0
.O
.O
275.0
25U
.O
.O
103.OF
144.OF
137.OF
.O
.O
.O
.O
384.0
26U
.O
.O
214.OF
201.OF
193.OF
.O
.O
-0
-0
608.0
2m
.O
.O
67.OF
66.OF
103.OF
7l.OF
.O
.O
.O
307.0
154.OF
147.OF
126.OF
12O.OF
2eJJ
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
547.0
29U
.O
.O
-0
.O
-0
.O
.O
.O
.O
.O
.O
3ou
-0
.O
.O
.O
.O
.O
.O
.O
.O
.O
31u
.O
-0
.O
-0
.O
.O
.O
.O
.O
32u
.O
.O
.O
.O
.O
.O
.O
.O
.O
-0
.O
5ou
.O
.O
.O
.O
.O
.O
.o
.O
.O
51u
.O
.O
'-0
.O
14
3
TABLE 7.2 BASE REFERENCE ASSIGNMENT SUMMARY INFORMATION SWMARY lNFORMAlION
CONTRAM
5.14 (16.
4.91)
RUN ON 11/ 6191
SMART CORR'IDOR BASE RUN NETWRK and TIME DATA (6/11/91) SMART
CORRIDOR
SMART
CORRIDOR
BASE
DEMAND CONTROL
DATA ITERATION
TIME SLICES 1
2
600
NUMBER
: 3
630
4
700
5
730
6
800
7
a30
a
900
930
9 13D0
1000
TOTALS 0
JOURNEY-TIME
OFREEMOVING
456.8
1222.2
1560.4
1650.4
1494.7
1099.3
700.3
520.1
43.1
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
140.3
543.3
1101.4
1144.8
465.2
65.4
37.4
1.2
3612.9
1362.5
2103.6
2831.8
2639.5
1564.4
765.8
557.5
44.3
12360.1
36981.5 98906.8 123665. 127436. 115655. 88215.8 56937.3 42310.3
3703.4
693810.9
FLOW DELAY PUEUEINC
33.8
TOTAL
490.7
DISTANCE
0 0
OVERALL
0
CVEH-H)
TRAVELLED
NETUORK
0 TOTAL
0
FINAL
0
CVEH-KM)
SPEED
75.4
8747.3
(KM/H)
72.6
58.8
45.0
43.8
56.4
74.4
75.9
83.6
56.1
3425.9
5910.4
4720.9
832.2
109.3
76.7
.O
15499.0
13395.8 12199.2
9563.2
6140.1
4530.5
408.9
73976.0
597.2
49.0
17.6
-0
4627.1
14969.3 13727.5 10160.5
6189.1
4548.1
408.9
78603.1
1445088
PUEUES CVEH)
69.2
354.4
0
0
FUEL
CONSUMPTION
OTRAVELLlNC
3952.4
PUEUEINC
14.3
TOTAL
LINK
OARRIVALS PCU
FACTOR
STOPS X
144.8
COUNTS
(VEIL) 257975
259595
234424
185418
121677
90337
8183
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
6046
17377
70741
77738
66095
36199
loo08
6701
247
291152
27.4
29.9
28.2
19.5
8.2
7.4
2.9
WITHIN
a.3 EACH
TOTAL
NUMBER
OF
PACKETS
TOTAL
NUMBER
OF
VEHICLES
TOTAL NUMBER OF PCUS PCU
1528.3
208733
7.7
SIZE
1573.5
78446
STOPPED
OPACKET
MEAN
702.3
3966.7 10675.5 13957.6
TOTAL
0
CLITRES)
10530.7 13255.3
FACTOR
O-D
MOVEMENT
ENTERING
THE
IS
ROUTES AND RCUTE
VARIABLE
NETWORK
20.1 MEMORY:
12735
MEAN LINKS PER RWTE
88529
WRDS
88529
USED PER ITERATION
1 . 0 0
75
AVAILABLE
CODONS PER WRD
16.32 3806309 ( la927 10
CUT
OF
3808437 )
3
8.0
INCIDENT MODELLING
The purpose of this chapter is to describe the techniques applied within the CONTRAM model and to report the results of modelling an incident on the freeway. The incidents modelled, the techniques applied within CONTRAM, and the results of the efforts are discussed within this chapter. The modelling of an incident on the freeway in conjunction with the modelling of in-vehicle information systems illustrates the benefits to both the equipped vehicles and non-equipped vehicles as well as system-wide results which will be reported in Chapters 9 and 10.
8.1
Incident Scenario
Two likely incident scenarios were created to evaluate the potential benefits of in-vehicle information systems. The key elements which comprise the modelling of an incident are:
1) 2) 3)
location; severity; duration.
8.1.1 Location To determine an appropriate location for an incident, the first consideration used was whether or not the location of the incident caused the freeway congestion to back up out of the initial boundary of the freeway or extend beyond the last time slice. If the congestion were to back out of the first subsection on the freeway, the results would be inaccurate as the number of vehicles in the system would not be comparable across different simulation runs. The second key consideration was to locate an incident in a subsection in such a manner as to cause a congestion pattern quite different from the normal congestion pattern. Thus, the subsections more towards the middle of the freeway section were given consideration as the location of an incident. Two different locations for two separate incident run scenarios were chosen to simulate. The first incident was located in subsection 20. The second run was made with an incident in subsection 22.
76
8.1.2 Incident Severity and Duration Many different types of incidents can occur on a freeway section. An incident on the freeway typically reduces the capacity of the subsection where the incident occurred. This capacity reduction can be at various levels and extend over time in different patterns. The following paragraphs describe the two separate incidents modelled. 8.1.2.1
Slight Incident
The first incident (hereafter referred to as the slight incident scenario), was modelled as occurring in subsection 20. The incident began in time slice 4 and reduced the capacity from 10900 vph to 7500 vph for the 30 minutes in time slice 4. The incident was modelled to last into time slice 5. During time slice 5, the capacity was reduced from 10900 vph to 8500 vph. This incident is the equivalent of having approximately one and a half lanes blocked in time slice 4, while in time slice 5, one-half lane is reopened. 8.1.2.2
Severe Incident
The second incident (hereafter referred to as the severe incident), was modelled as occurring in subsection 22. The incident began in time slice 3 and reduced the capacity from 12100 vph to 6900 vph for the one hour in time slices 3 and 4. This incident is the equivalent of having approximately two and a half lanes blocked.
8.2
Incident Modelling Within CONTRAM
The following sections outline the methodology and results of the slight and severe incident simulation runs. Along with results, conclusions are presented at the end of the chapter.
8.2.1 Methodology The following procedures were used to model an incident within CONTRAM. The first step in the process was to make a base run in CONTRAM with the capacity reduced in the subsection where the incident has occurred. This was accomplished through the use of Card Type 12. Card Type 12 allows the user to override any capacities calculated by CONTRAM except smaller reduced capacities arising
77
from blocking back. The. link number is entered followed by the capacity of the link in each corresponding time slice. A three-step process is conducted to model an incident within CONTRAM. The first step in the process is to make a base run (described in Chapter 7), whereby all drivers have 100 percent information and are taking their shortest routes. The second step involves the use of RODIN as described in Chapter 4. A RODIN run is conducted with no vehicles having information systems thereby being set to their fixed routes. The final step in the process is to make another CONTRAM run with the new network where the capacity of the subsection is reduced through the use of Card Type 12.
8.2.2 Results
Figure 8.1 presents the output queuing pattern on the eastbound Santa Monica for the slight incident scenario and the base run (described in Chapter 7). Based on a comparison of Figures 7.10 and 8.1 it can be concluded that the slight incident scenario does not change the queuing pattern substantially. It should also be noted that there is little increase in the amount of congestion as a result of the incident. Table 8.1 presents the network wide summary information for the slight incident as well. Figure 8.2 presents the output queuing pattern on the eastbound Santa Monica for the severe incident and the base run. As a result of the incident, the more queuing occurs and the pattern changes. There is a significant increase in the amount of congestion as a result of the incident. Table 8.2 presents the network-wide summary information for severe incident as well. As a result of the incident runs, questions began to arise as to both the severity and duration of the congestion as predicted by CONTRAM in both incident situations. Therefore, a FREQ analysis was conducted to compare the queuing pattern projected by FREQ to that provided by CONTRAM. Figure 8.3 presents the queuing pattern for both the slight and severe incident as predicted by FREQ. As seen in the figure, the congestion predicted by CONTRAM is not nearly as severe or as long lasting as that of FREQ. Due to time constraints, a thorough investigation as to why the discrepancies occurred could not be conducted. Thus, it was concluded that despite the fact that congestion was much less severe in CONTRAM than as predicted by FREQ, an analysis would be conducted using the severe incident scenario since, as a result of the incident, there was a change in the queuing pattern and an increase in the amount of congestion on the freeway. This deficiency in CONTRAM will be discussed later. Since the slight incident scenario did not result in much change in the queuing pattern on the freeway and not
78
FIGURE 8.1 QUEUING PATTERN - SLIGHT INCIDENT (EB SANTA MONICA)
...
LINK-BY-LINK
1
A L L - T I M E - S L I C E S - MEAN FINAL QUEUES (VW)
RUN ON lO/ 6/91
SHART CORRIDOR INCIDENT RUN NETWORK and TIME DATA (6/10/91) S M A RT CORRIDOR BASE
WART CORRIDOR
D EM A N D CO X guided) CONTROL DATA ITERATION
NUHBER 3
TIME SLICES : LINK
1
2
3
5
4
6
7
9
a
NO.8
TOTAL
TYPE
FINAL QUEUES 630
600
700
800
730
830
900
930
1300
1000
N
.O
.O
.O
.O
-0
.O
.O
.O
.O
.O
.O
191.0
668.0
94.0
.O
.O
au
.O
.O
.O
953.0 157.0
9u
-0
.O
55.OF
35.OF
67.OF
.O
.O
.O
.O
1ou
.O
.O
65.OF
42.OF
37.OF
.O
.O
.O
.O
.O
.O
103.OF
136.OF
131.OF
.O
.O
144.0
11u
.O
.O
12u
.O
-0
151.OF
136.OF
148.OF
.O
.O
370.0
.O
.a
.O
-0
276.OF
26O.OF
.O
.O
.O
.O
-0
.0-
44.OF
37.OF
.O
.O
786.0
14U
25O.OF 55.OF
435.0
13u
.O
.O
-0
.O
.O
35.OF
47.OF
.O
136.0
15u
.O
.O
.O
.O
.O
1.0
105.OF
105.OF
.O
.O
82.0
16U
-0
.O
211.0
1N
.O
.O
.O
16O.OF
16O.OF
-0
.O
.O
.O
.O
.O
.O
195.OF
168.OF
.O
.O
320.0
1BU
.O
.O
363.0
19u
.O
.O
.O
255.OF
244.OF
.O
.O
.O
.O
.O
.O
.O
122.OF
115.OF
.O
499.0
2ou
.O
.O
.O
.O
.O
.O
.O
.O
.O
237.0
21u
.O
.O
.O
-0
.O
.O
-0
28.0
.O
.O
.O
22u
.O
.O
23~
.O
.O
.O
82.0
108.OF
.O
.O
28.0
.O
.O
.O
.O
5.0
121.OF
145.OF
.O
.O
190.0
24u
.O
.O
271.0 328.0
25U
.O
.O
99.OF
109.OF
12O.OF
.O
.O
.O
.O
26U
.O
.O
184.OF
183.OF
182.OF
111.0
.O
.O
.o
.O
63.OF
107.OF
66.OF
.O
.O
.O
.O
12O.OF
121.OF
16O.OF
122.OF
.O
335.0
28U
:0 .O
99.OF
660.0
27-U
.O
.O
.O
.O
.O
.O
.O
.O
.O
523.0
29U
-0
.O
.O
.O
.O
.O
.O
.O
.O
.O
3ou
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
31u
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
32U
.O
.O
5ou
.O
.O
.O
.O
.O
.O
-0
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
51u
.O
.O
.O
79
FIGURE 8.2
QUEUING PA’ITERN - SEVERE INCIDENT (EB SANTA MONICA)
:.
LINK-BY-LINK
1
ALL-TIME-SLICES - MEAN FINAL ADDS WEHI
RUN ON 81 6191
SMART CORRIDOR INCIDENT RUN NETWORK and TIME DATA (6/8/91) WART CORRIDOR INCIDENT RUN NETWORK and TIME DATA (6/8/91) CONTROL DATA
SMART CORRIDOR
ITERATION TIME SLICES : 1 2
LINK
3
4
5
6
7
8
NUHSER 3
9
NO.8
TOTAL FINAL PUEUES
TYPE 600
630
700
730
800
830
900
930
1000
1300
7u
.O
.O
.O
.O
.O
.O
-0
.O
.O
.O
8u
.O
.O
502.0
1803.0
1308.0
398.0
.O
.O
.O
4011.0 186.0
9u
.O
.O
44.OF
61.OF
43.OF
38.OF
.O
.O
.O
1ou
.O
.O
42.OF
35.OF
62.OF
37.OF
.O
.O
.O
176.0
11u
.O
.O
107.OF
105.OF
103.OF
117.OF
.O
.O
.O
432.0
12u
.O
.O
12l.OF
125.OF
121.0F
120.OF
.O
.O
.O
487.0
13u
.O
.O
273.0F
25O.OF
259.OF
250.OF
.O
.O
.O
1032.0
14u
.O
-0
37.OF
37.OF
64.OF
45.OF
.O
.O
.O
183.0
15u
.O
.O
39.OF
51.OF
28.OF
.O
.O
.O
.O
118.0
16U
.O
.O
123.OF-
97.OF
94.OF
.O
.O
.O
.O
314.0
1N
.O
-0
177.OF
166.OF
43.0
.O
.O
.O
.O
386.0
18U
.O
.O
173.OF
195.OF
176.OF
.O
.O
.O
.O
544.0
19u
.O
.O
286.OF
254.OF
264.0~
148.0
.O
.O
.O
952.0
2ou
.O
.O
96.OF
87.OF
103.OF
94.OF
.O
.O
.O
380.0
91.OF
99.OF
.O
.O
.O
415.0
.O
36.0
.O
.O
.O
468.0
.O
.D
129.OF
.O
.O
.O
129.0
.O
71.0
13O.OF
.O
.O
.O
201.0
21u
.O
.O
108.OF
117.OF
22u
.O
.O
2OO.OF
232.OF
23U
.O
-0
-0
24U
.O
.O
-0
25U
.O
.O
.O
.O
94.OF
98.OF
.O
.O
-0
192.0
26U
.O
.O
.O
.O
186.OF
182.OF
.O
.O
.O
368.0
27-U
.O
.O
.O
.O
62.OF
66.OF
-0
.O
.O
128.0
28U
.O
.O
.O
.O
120.0F
137.OF
.O
.O
.O
257.0
29U
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
3ou
.O
.O
.O
.O
.O
.D
.O
.O
.O
.O
31u
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
32U
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
5ou
-0
.O
.O
.O
-0
.O
.O
.O
.O
.O
51u
.O
.O
.O
.O
.O
.O
.O
.O
.O
.O
80
_
(FREQ)
QUEUING PAlTERN - ilt;&iiiiD SEVERE INC. COHTWR D I A G R A M O F GUEUE L E N G T H BEFORE ENTRY CONTROL
^ .
SLIGHT INCIDENT
BEGIN
TIM S L I C E m.......-.. . .._....................... . . . . . . . . . . . . . . . . . . . . . . . ..-............................-......... a . .
5
.
.tttttt.*t*tt
. -
**t*,tt*tttt*ttt~t**~~*~*~* l
l
tt***t**ttt**t**t*t*~*~**,~.~~~~..~~**
3.
+****t**tt***
.
1
.
**
ttt.*tt***ttt**t**************~*
4 .
2
9:30 9:oo
. -
7. 6
TIHE
ttttttttt
ttttttt*.t*tt****t.~. t*t***t~tt**~tttt~********
.
-
8:30
.
-
8:OO
.
-
7:30
Htttt.ltftt*tHlttttt~~..~~~~~*~*~* . . . +**tttttt*t~****t
. -
7:oo
. -
6:30
.
6:00
-
. . . . . . . . . . . . . . . . . ..-........ . .._...................................................................... : 01
.
0
::
2
:
03
:
:
04
BLANK DENOTES MOVING TRAFFIC.
::::
06
05
:
07
( W H E N B O T H PUEUES
::: 11
12
: 13
:
16
:
17
::: 18
::
22
19
A S T E R I S K D E N O T E S P U E U E O V E H I C L E S D U E T O HAINLINE
H D E N O T E S CMEUED V E H I C L E S D U E T O M E R G I N G . MERGING.
: 08
::
::
24
::
26
:
:
20
CONGESTION.
B DENOTES PUEUEO VEHICLES DUE TO MAINLINE CONGESTION AND
EXIST, LENGTH OF DISPLAY REPRESENTS MAINLINE CONGESTION.)
CONTOUR DIAGRAM OF GUEUE LENGTR BEFORE ENTRY CONTROL
SEVERE INCIDENT BEGIN
TIME
TIflE
SLICE -
9:30
-
9:oo
-
8:30
-
a:00
7:30 7:oo 6:30 -
-
. . . . .._................. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..-............................-........ . 01
02
:
::
03
04
05
B L A N K D E N O T E S MOVlNG T R A F F I C .
:
:
::::
06
07
08 1 1 1 2 1 3
:
:::
:
16 17
: 10
:
:::
19
A S T E R I S K D E N O T E S GUEUED MHlCLES D U E 70 MlNLiNE
R D E N O T E S PUEUED V E H I C L E S D U E T O M E R G I N G . MERGING.
:
::
22
::
24
::
26
::
28
CONGESTION.
B DENOTES QUEUED VEHICLES DUE TO MAINLINE CONGESTION AND
(UHEN B O T H CIUEUES E X I S T , L E N G T H O F D I S P L A Y R E P R E S E N T S M A I N L I N E CONGESTIOH.)
81
:
:
6:00
TABLE 8.1 SLIGHT INCIDENT ASSIGNMENT SUMMARY INFORMATION SUNMARY INFOARATloN
1
CONTRA!4 5 . 1 4 (16. 4 . 9 1 )
R U N O N lO/ 6/91
SHART C O R R I D O R I N C I D E N T R U N NETWJRK and T I M E D A T A (6/10/91) SMART
CORRIDOR BASE DEMAND
(0 X g u i d e d )
CONTROL DATA
SMART CORRIDOR
ITERATION NUMBER 3 TIME SLICES : 3
2
1 630
600
5
4
700
6'
800
730
7
9
a
900
a30
930
1300
1000
TOTALS 0
JOURNEY-TINE WEH-HI
OFREEMOVlNG
456.9
1219.3
1565.1
1584.7
.O
.O
.O
.O
33.9
153.9
610.1
1222.5
1324.3
490.8
1373.2
2175.3
2807.2
2771.0
FLOW DELAY OUEUEING TOTAL 0
1188.9
.O
706.7
.O
-0
562.4
84.9
1751.3
520.6
43.0
.O
.O
.O
1.1
4029.9
44.1
12762.5
3696.9
693233.3
36.8
791.5
557.3
8732.6
DISTANCE TRAVELLED WEH-KM) 369R.Q 98592.9 123565. 122527. 112795. 95287.0 57441.4 42351.5
0 0
1447.5
O V E R A L L NETWRK SPEED (KM/H)
0
75.3
0
T O T A L F I N A L OUEUES WEH)
0
68.8
56.8
43.6
40.7
54.4
72.6
76.0
83.8
54.3
3301.3
7050.8
6403.4
1334.8
110.5
74.7
.Q
18747.4
1 1 9 7 5 . 5 10304.3
73764.9
71 . a
403.1
0 0
F U E L CONSUPIPTION (LITRES)
OTRAVELLING OUEUEING TOTAL 0
3 9 5 1 . 3 1 0 i 8 3 . 3 1 3 1 1 0 . 4 12805.8 14.4
161.7
795.0
1624.2
1765.6
3965.6 10645.0 13905.4 14430.0 13741.1
6191.4
4535.1
408.0
723.0
77.0
16.8
.O
5177.7
11027.3
6268.4
4551.8
408.0
78942.7
1443869
T O T A L L I N K CWNTS (VEH) 78427
207885
257125
250230
198248
122490
90386
8467
PCU FACTOR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
STOPS
6052
17427
65667
70937
68376
45214
11000
6663
242
291582
7.7
6.4
25.5
28.3
29.6
22.8
9.0
7.4
2.9
20.2
OARRIVALS
I STOPPED
230611
ROUTES iN0 RUJTE HEWRY:
OPACKET SIZE UITHtN EACH O-D MOVEMENT IS VARIABLE TOTAL NUMBER OF PACKETS ENTERING THE NETWORK
12735
HEAR LINKS PER RWTE
T O T A L NUMBER OF VEHICLES
88529
WRDS AVAILABLE
T O T A L NUHEER OF PCUS
88529
USED PER ITERATION
MEAN PCU FACTOR
1
.
82
0
0
CCOONS P E R WRD
16.31 3806309 la.561 10
( OUT OF
380‘3437 )
TABLE 8.2 SEVERE INCIDENT ASSIGNMENT SUMMARY INFORMATION
:.
.
SUW~RY
1
INFORMAllOY
CONTRAM 5 . 1 4 ( 1 6 . 4 . 9 1 )
R U N O N 8/ 6/91
S M A R T C O R R I D O R I N C I D E N T R U N NETVORK a n d T I M E D A T A (6/8/91) S M A R T C O R R I D O R I N C I D E N T R U N NETUORK a n d T I M E D A T A (6/a/91) CONTROL DATA
SMART CORRIDOR
lTERATlON N U M B E R 3 :
TIME SLICES 2
1 600
3
5
4
700
630
730
6
a
7
830
800
900
930
9 1000
1300 TOTALS
0 J O U R N E Y - T I M E (VEH-HI OFREEHOVING
456.9
FLOW DELAY
1475.7
1475.9
1256.6
001.8
521.1
43.0
.O
.O
.O
.O
.O
.O
.O
.O
153.9
691.7
1715.0
2275.9
1342.8
202.5
44.0
1.1
6460.8
1373.2
2174.0
3190.6
3751.8
2599.4
1004.3
565.1
44.2
15193.5
65549.4 42385.6
36W.0
693232.6
490.8
TOTAL
0732.6
D I S T A N C E T R A V E L L E D (VEH-KM) 3 6 9 7 7 . 0 9 8 5 9 4 . 2 1 1 6 4 8 0 . 1 1 3 5 6 7 . 115141.
0
0
1482.4
.O
33.9
PUEUEINt
0
1219.3
.O
100840.
O V E R A L L NETUURK S P E E D ( K M / H ) 71 .a
53.6
35.6
30.7
38.8
65.3
75.0
83.7
45.6
4417.5
8404.4
7776.9
3594.7
174.6
76.1
.O
24916.1
12574.7 11162.2
7177.9
4539.2
408.2
74877.4
097.3 2281.5 3083.0 1808.7 14.4 161.7 3965.6 10647.9 13471.3 14285.2 15657.7 12970.9
235.5
26.1
.O
8508.1
7413.4
4565.3
408.2
03385.5
1443869
0
75.3
0
T O T A L F I N A L PUEUES CVEH)
0
68.8
403.1
0
0
FUEL CONSUMPTION (LITRES)
OTRAVELLING PUEUEING TOTAL
0
3 9 5 1 . 3 10486.2
12574.1
12003.8
T O T A L L I N K CWNTS CVEH) 70427
207885
241592
230516
235125
210443
140889
90521
8471
PCU FACTOR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
STOPS
6052
17427
57331
60150
84629
66404
15728
6iLi4
243
314700
7.7
a.4
23.7
26.1
36.0
31.6
11.2
7.4
2.9
21.8
OARRIVALS
X STOPPED
OPACKET S I Z E W I T H I N E A C H O - D M O V E M E N T I S V A R I A B L E TOTAL NUMBER OF PACKETS ENTERING THE NETWORK
12735
MEAN L I N K S P E R ROUTE
TOTAL NUMBER OF VEHICLES
88529
VORDS AVAILABLE
TOTAL NUMBER OF PCUS
85529
USED PER ITERATION
MEAN PCU FACTOR 1
ROUTES AND ROUTE MEMORY:
CONVERGENCE MONITOR -
1.00
UW)ONS P E R VORD
lb.31 3806309
( OUT OF
10
S U M M A R I E S O F J O U R N E Y - T I N E S . D I S T A N C E S A N D CIIANGFS - FOP AI, TTFPATT~YP
83
3808437 )
18710 RUN ON 87
b/91
much increase in congestion, it was determined that the remaining analysis would focus on the results provided by the severe incident.
84
9.0
SIMULATION RESULTS UNDER SEVERE INCIDENT SCENARIO
The purpose of this chapter is to describe the methodology and analysis results of the investigation into the benefits derived from in-vehicle information systems under the severe incident scenario. The first part of the chapter describes the methodology and techniques used to obtain results from the modelling process. The results are then broken down into both system-wide performance measures and benefits for both guided and unguided vehicles.
9.1
Methodology: Modelling Guidance Systems in CONTRAM
In order to model guidance systems within the CONTRAM model the RODIN program as described in Chapter 4 was used. RODIN is external software program developed by Nick Taylor (TRRL) and is intended to simulate route guidance. This program converts a packet route file output produced by a standard CONTRAM into an origin-destination matrix and a set of routes which it embeds as fixed routes in a copy of the network file. The origin-destination movements are duplicated and each set is preceded by a percentage multiplying or split factor. The network and demand files are then rerun in RODIN. The first set of origins-destinations is assigned on the fixed routes (i.e. along the original routes, which are the minimum time path routes before the incident), while the second set is assigned to minimum cost routes. This provides a framework in which experiments involving two user classes (guided and unguided vehicles) can be performed. RODIN is also designed to provide: 0
0 0
a choice of methods for setting packet sizes; alternate vehicle class for the second set of 0-Ds; randomization of the output O-D counts.
An important point about this procedure is that the fixed and free routed trips are dynamically integrated so that each affects the routes of the others in an expected way. Once a RODIN base run under the severe incident scenario with 0 percent equipped vehicles was complete, a series of CONTRAM runs with varying percentages of equipped vehicles was chosen to be evaluated under the severe incident scenario. The percentages of in-vehicle information equipped vehicles chosen for examination was 0, 10, 25, 50, 75, 90 and 100. It was felt that a range of 0 to 100 with five points in-between would identify where the benefits would be the greatest and/or would describe any trends that may develop.
85
9.2
Analysis Results
An analysis of system-wide benefits and benefits to the users and non-users of the in-vehicle information system was conducted. The first step in the process was to evaluate the system wide results via the system-wide measures output from each CONTRAM run for the varying percentages of equipped vehicles under the severe incident scenario. The second phase of the evaluation was through the use of UFPASC as described in Chapter 7.
9.2.1 System-Wide Results Table 9.1 displays the results from a system-wide perspective. As seen in the table, the results of the simulation under the severe incident situation indicate that 100 percent of the vehicles on the road equipped with in-vehicle information systems provides the greatest benefit to the system in terms of total system travel time, travel time per vehicle, and speed. Total system travel time is perhaps one of the most important measures of system-wide performance. The total system travel was 12,360 vehicle-hours under the non-incident base run. Under the severe incident scenario but with all unguided vehicles, the total system travel time increased to 15,194 vehiclehours, a difference of 2,834 vehicle-hours. As the percentage of guided vehicles increased under the incident scenario, the total system travel time decreased from 15,194 to 13,101 vehicle-hours, a reduction of 2,093 vehicle-hours. This would indicate that the adverse effect of the incident was significantly reduced under guided vehicle situations. In addition to the varying percentages of equipped vehicles under the severe incident scenario, the base run without the incident where all vehicles choose their fastest route is shown. As seen in the table, at 100 percent, equipped vehicles under severe incident conditions, the speeds are only slightly lower and the travel times are only slightly higher than those under the no-incident, 100 percent, guided base run. It also appears as though at either 50 percent equipped vehicles, or 75 percent equipped vehicles, a quirk in the data occurred. For all other percentages between 0 and 50 and 75 to 100 the findings were consistent in that the more equipped vehicles, the more benefit was accrued to the system. However, the data between 50 and 75 percent equipped did not follow that trend. The quirk in the data will be pursued in future research as was not possible in this project.
86
Table 9.1 System-Wide Results .O’-
Percent Vehicles Eq@p&
‘.
.’
10
25
;
‘.. ”
:’
‘.
50
.75
‘90
100 . . Base Run
Avg. Travel Time per Veh (min)
10.30 10.00 9.86
9.44
9.52
9.26
8.88
8.38
Avg. Travel Distance per Veh (mi)
12.53 12.48 12.43
12.48
12.45
12.52
12.59
12.54
Average Speed (mph)
28.5
29.2
31.0
30.7
31.7
33.3
35.1
29.6
I,
Figure 9.1 displays the increase in average speed, network-wide under the severe incident scenario. As seen in the figure, an increase of approximately 17 percent is obtained for 100 percent informationsystem-equipped vehicles. The average travel time per vehicle is displayed in Figure 9.2. The travel time per vehicle on average is network wide from each O-D pair. As seen in the figure, the average travel time per vehicle decreases from slightly more than 10 minutes to less than 9 minutes for 100 percent equipped. This represents a reduction of approximately 14 percent over the nine-mile-long, two-mile-wide corridor for each vehicle on average, as presented in Figure 9.3. It is difftcult to make conclusive remarks regarding the time saved per vehicle because the average trip length is not very long. An average trip length of over 20 to 25 minutes is more desirable. However, the system-wide results provided by this analysis indicate that the greatest benefits are obtained when all vehicles are equipped with in-vehicle information systems. Assuming optimal data is given to all drivers and that all drivers follow their recommendations. Figure 9.4 presents the percent decrease in total queues, network-wide under the severe incident scenario. As seen in the figure, a decrease of almost 35 percent is obtained with 100 percent of the vehicles having in-vehicle information. From the figure it can be concluded that the guided vehicles are diverting to avoid the major queues. Whether or not travellers purchase the IVHS equipment and the various percentages of equipped vehicles occur, depends on two major factors. First, do “IVHS” guided vehicles benefit significantly enough to justify their expenditure of funds for such equipment, and second, will all users and the general public be supported by governmental-supported traffic control centers? In order to begin to address these 87
FIGURE 9.1
Percent Increase in Speed Severe Incident (Network-Wide)
20
30
40
50
60
70
Percent MS Equipped Vehicles
80
90
100
FIGURE 9.2
Travel Time Per Vehicle (Network-Wide) Severe Incident
8.80
0
I 10
1 20
1 30
I 40
I 50
I 60
I 70
% MS Equipped Vehicles
89
I 80
I 90
100
FIGURE 9.3
% Travel Time Saved (Network-Wide) Severe Incident 14.00
1200
8.00 8 6.00
200
0.00
1
10
I
20
I
30
I
40
I
50
I
60
I
70
% MS Equipped Vehicles
90
I
80
I
90
1
100
FIGURE 9.4
% Decrease in Total Queues (Network) Severe incident 30.00
0
IO
20
30
40
50
60
70
% MS Equipped Vehicles
91
80
90
100
questions, benefits to travehers in guided and unguided vehicles need to be assessed. This initial assessment is discussed in the next section. All benefits are also based on the availability of substantial un-used capacity on the parallel arterials.
9.2.2
Benefits to Guided and Unguided Vehicles
To properly evaluate the benefits to both the guided and non-guided vehicles in the system, a procedure was used whereby two classes of vehicles were set up. The first set of vehicles were those that were considered guided, which were free to be assigned on the fastest routes within CONTRAM. The second class of vehicles were the unguided vehicles, which were the vehicles assigned to the fixed routes that did not change regardless of the incident on the freeway. The primary source of information regarding the benefits that both guided and unguided vehicles obtained with the varying percentages of equipped vehicles under the severe incident scenario on the network came from the UFPASC. The UFPASC (User Friendly Post Analysis System for CONTRAM) is an interactive program for examining the outputs from CONTRAM runs. The UFPASC produces tabular and graphical outputs for selected parameters from the results file produced by CONTRAM. UFPASC uses a menu system to set up the analysis stages for producing the selected outputs. UFPASC allows selective investigations of the .RTE and .PAF files produced by CONTRAM. Thus, by using the UFPASC, a number of origin-destination pairs were evaluated. The origin-destination pairs were chosen on the basis of three key considerations:
1)
The O-D pair had to have a reasonably large demand level, at least enough to draw some reasonable conclusions;
2)
The O-D pair had to have the incident on the freeway between the origin and destination. This is a requirement so that there is the opportunity for some significant diversion to occur;
3)
Different O-D pairs would be chosen relative to one another so that a cross section of the different areas of the network would be examined and that both Adams Boulevard and Washington Boulevard would have the opportunity to be used.
On this basis three different O-D pairs were chosen. The three different O-D pairs are highlighted in Figure 9.5. The first O-D pair chosen for evaluation was that of origin 6 to destination 14. Origin 6 is off the arterial Washington Boulevard while destination 14 is at the end of the Santa Monica eastbound freeway, The second O-D pair chosen was origin 1 on the western boundary of the Santa Monica 92
FIGURE 9.5
O/D Pairs Chosen For Evaluation
Legend
0 xx
- Origin/Destination
* - Location of Severe Incident
93
freeway to destination 15 at the eastern end of Washington Boulevard off Figueroa. The third origindestination pair chosen for evaluation was origin 25 off Adams Boulevard to destination 14. Thus, in the three O-D pairs, two originate on the arterial with their final destination being the freeway, while the other pair originates on the freeway and ends on the arterial. Table 9.2 presents the results for the three origin-destination pairs chosen for evaluation in terms of average speed (mph) and average travel time (min) for both guided and unguided vehicles with the varying percentages of in-vehicle information equipped vehicles. This information is an average over all time slices. Time slice by time slice information is only available for the routes selected, not for the route time, distance, and speed. Figures 9.6 - 11 present the information from Table 9.2 in graphical form. As seen in the figures, the guided vehicles have a higher speed and shorter travel time to the destination than the unguided vehicles. Once again, as in the system-wide results, the highest benefits in terms of speed and travel time were found to occur with 100 percent in-vehicle information equipped vehicles. The reliability of the total route distance for both types of vehicles was found to be questionable. It appeared that the same route did not have the same distance in different runs; therefore, the total route distance was not used as part of the analysis. However, analysis was conducted to investigate the routes chosen between different origin-destination pairs for varying percentages of equipped vehicles. Figure 9.12 presents the results of such analysis. The figure shows the two most popular routes chosen for the unguided vehicles and guided vehicles between origin 1 which is on the western boundary of the freeway, and destination 15 which is on the eastern boundary of the network at the end of Washington Boulevard. The figure represents the routes chosen when 75 percent of the vehicles are equipped with in-vehicle information systems under the severe incident scenario. The two most popular routes for the unguided vehicles both travel through the incident on the eastbound Santa Monica Freeway, while for the guided vehicles, the second route of choice was to exit the freeway at the first possible alternative and use Washington to reach destination 15.
9.3
General Evaluation
The following assumptions need be noted again to cautiously view the results of this analysis:
94
FIGURE 9.6
Average Speed (mph) Origin 5006 to Destination 9014
26.00
25.00
0
I 10
1
20
,
30
I 40
I 50
L 60
1
70
% MS Equipped Vehicles
I -a- Guided Vehicles
--I-- Unguided Vehicles -+-- Overall
95
I 80
I
90
1
FIGURE 9.7
Average Travel Time (min) Origin 5006 to Destination 9014 13.20
1 ,*6@ ___.._ _ _._. _ ___.__ _ _______.__.._____ _ ___._._.___...__._._................._..._ . . . . . . . . . . . . . . . . . . ..-.-.....- _ . . . . . . . . . . . . . . ..-...._ . . . . . . __
0
IO
20
30
40
50
60
70
% IVIS Equipped Vehicles
--m- Guided Vehicles
+ Unguided Vehicles + Overall
96
80
90
100
FIGURE 9.8
Average Speed (mph) Origin 5001 to Destination 9015 3200
26.(,(,- . . . . . . . . . . . . . . _.
_ ...._ _ ._ _._ _ _ . _ . _._ . __._._ ___ _._._._. . ._. . . . . . . _ . _. _. _ . . . . . . . . . . . . . -.._. _-.__ ._..._--_-. _ _.. ._. . __._.___.._. _ . . __^._. _ -...
23.00
0
I
10
I
x)
30
1
40
I
50
I
60
I
70
L
80
% IVIS Equipped Vehicles
I -m- Guided Vehicles
-+- Unguided Vehicles + Overall
97
,
90
I
1
loo
FIGURE 9.9
Average Travel Time (min) Origin 5001 to Destination 9015
0
1’0
2’0
3’0
40
E;o
so
io
so
% IVIS Equipped Vehicles
-m- Guided Vehicles
-+- Unguided Vehicles +K- Overall
-_
98
40
160
FIGURE 9.10
Average Speed (mph) Origin 5025 to Destination 9014 33.00 3200
27.00 26.00
% MS Equipped Vehicles
-c- Guided Vehicles
-+- Unguided Vehicles +-- Overall
99
FIGURE 9.12 ROUTE CHOICE - GUIDED, UNGUIDED VEHICLES
Top
Two Route Choices - Unguided Vehicles Origin 1 to Destination 15
Legend
0
xx
- Origln/Destinotion
e - Location of Severe Incident
Top Two Route Choices - Guided Vehicles Origin 1 to Destination 15
- Origin/Destination *
- Location of Severe Incident
101
0
Only two of the five parallel arterials within the Smart Corridor were modelled; additionally, three-quarters of the entire length of the Corridor was modelled. Thus, the travel times throughout the corridor are not as large as one would like to evaluate the benefits.
0
The structure of the origin-destination pairings was set up so that all origins and destinations were external to the network. The origin and destination data were very coarse and from point to point, each origin and destination could be reached by either the freeway or the arterial, not both. For example, to get to destination 15, a vehicle must exit the freeway and use Washington Boulevard.
0
Due to the weaknesses in freeway modelling and incident modelling as described in Chapters 6 and 8, the results presented from this analysis should be viewed with caution. The travel times on the freeway are too low during both incident and non-incident conditions thus, diversion to the arterial is not as attractive as it should be.
0
Since this is the first application of RODIN, the results should be viewed with some caution as well.
0
The simulation results in this analysis apply only to the morning peak period. Additionally, the type of incident modelled is atypical and extreme, since it is unlikely that two-and-a-half lanes would be blocked for longer than one-half hour. All analysis for guided versus unguided vehicles was conducted on the traffic heading eastbound toward downtown Los Angeles.
103
10.0 OVERALL ASSESSMENT AND FUTURE RESEARCH This chapter describes the overall assessment of the completed research. First, the suitability of CONTRAM is described followed by a description of weaknesses of the study outside of the model; then the major findings of the study are presented. Finally, potential future directions regarding the application of a modelling process to evaluate the potential benefits of in-vehicle information systems are discussed.
10.1 Suitability of CONTRAM The CONTRAM model has many features that are well suited to an application such as this. However, there still remain questions about some of its capabilities. This section outlines the strengths and weakness of the model for this particular application.
10.1.1 Strengths
1)
When using the 386 version of CONTRAM 5 and DBOS memory management system, there was no problem in terms of network size limitations and running time for this particular application.
2)
CONTRAM provided evidence of accurate representation of oversaturated conditions on arterials.
3)
The use of COMEST in developing O-D matrices was very helpful. The difficulty of setting up a realistic time-sliced O-D matrix is traditionally one of the main problems encountered in an experiment such as this one.
4)
RODIN provided the capability to distinguish between guided and unguided vehicles. The unguided vehicles were always assigned to the same fixed routes determined by the average daily conditions, without incident. These routes were usually no longer optimum under incident conditions. On the other hand, guided vehicles are assigned to their optimum routes, assuming perfect knowledge of current traffic conditions.
104
10.1.2 Weaknesses - Suggested Improvements
1)
The weakness of CONTRAM in the area of representation of oversaturated conditions on freeways was identified as the most important problem with regard to this application. Although a lot of time was spent in trying to get acceptable results, as described in Chapter 6, many questions were not answered: 0
What type of link (uncontrolled, give-way or signalized) is the most appropriate to code freeway links and on-ramps? As mentioned in Chapter 6, this decision has an effect on the queuing process.
0
How does the concept of “throughput capacity” used by CONTRAM relate to the usual concept of freeway capacity?
0
How can the speed-flow relationships of CONTRAM be used in simulating oversaturation conditions?
0
Is it possible to do any weaving analysis?
0
Some parameters, like the jam headway spacing and points on the speedflow curve can be calibrated on a simple freeway section. How can the calibration information be used when applying the model to a complex corridor network?
2)
The CONTRAM standard assignment assumes that all vehicles take their optimum routes. The base run (no incident) should not be regarded as an accurate representation of average daily conditions. Some drivers do not choose the best route, and this should be accommodated by the model by introducing distortions to the perceived link costs.
3)
Under incident conditions, it was assumed that unguided vehicles always take the same route that used to be optimum under non-incident conditions. It would be realistic to model the spontaneous diversion of unguided vehicles due to sources of information other than the guidance system, like radio information or the direct view of queues in front of them.
4)
The use of RODIN for varying percentages of guided vehicles led to some discrepancies in the demand structure and the total number of vehicles in the system (in the range of 3%). In order to have some comparable results, the demand should be exactly the same. 105
The truncation of link travel times led to some problems in speed calculations. For example, when using a link free-flow speed of 80 km/h in undersaturated conditions, the resulting average speed is not necessarily 80 km/h, as expected. 6)
7)
10.2
Output information useful for this type of analysis which is not provided by CONTRAM such as: 0
aggregate statistics per set of links (Freeway-Arterials);
0
aggregate statistics per vehicle type (Guided-Unguided)
Although the use of UFPASC (described in Chapter 9) for routing analysis was very helpful, some weaknesses were identified: 0
The run time for each O-D pair is a half hour to an hour on a 386 microcomputer;
0
The reliability of the total route distance is questionable, as it appeared that the same route did not have the same distance in different runs;
0
A time slice by time slice analysis would be a useful tool in comparing travel times and travel distances;
0
A network plot showing the two most popular routes for a given O-D pair, vehicle type and time slice would be very useful as well.
Weaknesses of the Study
The improvement of modelling freeway congestion does not alone cure the weaknesses of the study. Weaknesses exist on both the supply and demand side of the modelling process as well. With respect to the supply side, as mentioned in Chapter 5, the Smart Corridor consists of five parallel arterials with approximately 15 cross streets, and is approximately 13 miles long. Due to time and resource limitations, approximately nine miles of the SMART Corridor with two parallel arterials was coded into the model instead of the entire 13 miles and five parallel arterials of the corridor. The eastern boundary of the network is the Harbor Freeway, while the western boundary is LaCienega Boulevard. The two parallel arterials coded were Washington Boulevard and Adams Boulevard. Ten of the 15 major north-south streets connecting Washington, Adams, and the Santa Monica Freeway were coded as well. Thus, with 106
only two parallel arterials the opportunity for diversion is not as great as it would normally be in the Smart Corridor. Additionally, since approximately nine miles of the 13 mile corridor were modelled, the travel times throughout the corridor were not as high as they would normally be due to the reduced travel distance, and diversion from the freeway to the arterial was not as attractive as it might be with a longer travel time. From the demand side, problems exist with both the origin-destination matrix and the structure of the demand over time. The problems with the origin destination information and structure are described in Chapter 7. Only external sinks and sources were used; thus, any major internal sinks and sources within the corridor were neglected. Although using the origin-destinationestimator program, COMEST, assisted in creating a somewhat realistic origindestination matrix, it should be pointed out that the inputs to COMEST are the flows instead of the actual demands. Thus, the demand could be underestimated through use of this program. A second weakness in the demand structure is the variation over time. Based on information provided by the previous study [3] and information regarding the performance of the system, the demand was manipulated to create an origin-destination matrix for eight time slices. Information was only available for the time period of 7:00 a.m. to 8:00 a.m., and all information for the previous and remaining time slices was factored based on spot trafftc counts. Thus, for the purposes of this study the highest demand was assumed to occur from 7:00 a.m. to 8:00 a.m. while the demand for the other time slices were factored based on information provided from the previous study. Once again, this is likely not the case and leaves the results of the study open to some question.
10.3 Major Findings
Based on the information provided in the previous two sections, the results of this study should be viewed with some caution. The results should be viewed in a somewhat qualitative manner, the findings being the more vehicles equipped, the better the system performance. As seen in Table 9.1, with a severe incident on the freeway, as the percentage of vehicles equipped with information increases, the performance of the system improves to where the system is at a level of performance that is only slightly less than that before the incident occurred. This degree of improvement is only possible because of the availability of underutilized capacity on the arterials parallel to the freeway. Chapter 9 describes in greater detail the results of the analysis.
10.4 Future Research
1)
The representation of oversaturated conditions on freeways. As previously mentioned,
some difficulties were encountered in the modelling of the freeway portion of the 107
SMART Corridor. Any further application of CONTRAM to this type of network will have to carefully address this question. The authors of the model reported that Southampton University has recently conducted research on modelling congestion on freeways by varying the randomness parameter in the queuing formula [23]. This is similar to the approach suggested some years ago by Davidson [24] and could be repeated using the current version of CONTRAM. However, it is not clear how relevant this method is. 27re use of ROGUS. ROGUS is a program currently under development at TRRL to simulate route guidance based on the CONTRAM model. Because of time constraints it was not possible to use ROGUS in this phase of the study, though it could be used in an extension of the project. ROGUS consists of two main parts: Modified CONTRAM and ROGUWAda.
a>
Modified CONTRAM is responsible for generating the base loading of unguided vehicles, and differs from standard CONTRAMS only in its ability to distort drivers’ perception of link costs to simulate their lack of perfect information, and
b)
ROGUS/Ada reassigns a certain proportion of the unguided vehicles to simulate guided vehicles. The method of assignment is event-based using simulated beacon information which itself is based on the historical data and simulated real-time data from detectors and vehicle-to-beacon communications.
Ihe application and simulation of d$erent trajk management strategies. It would be interesting to compare the effects of guidance systems with some other corridor management strategies like signal optimization and coordination to determine how the benefits compare and if the benefits are cumulative. laze potential re-investigation of the IIVlEGRAlTON model. The model is now available for investigation. It might be worthwhile to reevaluate the application of the INTEGRATION model to the Smart Corridor.
108
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2.
Jeffery, D.J., Russain, K., and Roberston, D.I., Electronic Route Guidance bv AUTOGUIDE: The Research Background, Traffic Engineering and Control, Vol. 28, No. 10, October 1987.
3.
Al-Deek, H.M., Martello, M., May, A.D., Sanders, W., Potential Benefits of In-Vehicle Information Svstems in a Real-Life Freewav Corridor under Recurring and Incident Induced Congestion, PATH Research Report UCB-ITS-PRR-89-1, 1989.
4.
Kanafani, Adib, Towards a Technologv Assessment of Automated Highwav Navigation and Route Guidance, PATH Working Paper, University of California, Berkeley, December 1987.
5.
Gosling, G.D., AResearch Research Report, ITS, University of California, Berkeley, 1988.
6.
May, A.D., Navigation/Communication Technology Assessment, Research Report, ITS, University of California Berkeley, May 1988.
7.
Skabardonis, A., Control Strategies and Route Guidance, Research Report, ITS, University of California Berkeley, May 1988.
8.
Gosling, G.D., Artificial Intelligence Techniaues for Network Flow Management, Research Report, ITS, University of California, Berkeley, May 1988.
9.
Sullivan, E., Wong, S., Development of a Dvnamic Eauilibrium Assignment Procedure for Network Level Analvsis of New Technolopv, PATH Research Report, ITS, University of California, Berkeley, 1989.
10.
Al-Deek, H., May, A.D., Potential Benefits of In-Vehicle Information Svstems (IVIS): Demand and Incident Sensitivitv Analvsis, PATH Research Report UCB-ITS-PRR-89-1, 1989.
11.
May, A.D., The Highwav Congestion Problem and The Role of In-Vehicle Information Svstems, Submitted to General Motors Conference, April 1989.
109
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Gardes, Y., May, A.D., Traffic modelling to Evaluate Potential Benefits of Advanced Traffic Management and In-Vehicle Information Svstems in a Freewav/Arterial Corridor, PATH Research Report UCB-ITS-PRR-90-3, 1990.
13.
Taylor, N.B., CONTRAM 5: An Enhanced Traffic Assignment Model,, TRRL Research Report 249, 1989.
14.
CONTRAM. UserGuides, TRRL, December 1989.
15.
Hall, M.D., Van Vliet, D., Willumsen, L.G., SATURN - A Simulation - Assignment Model for the Evaluation of Traffic Management Schemes, Traffic Engineering and Control, Vol. 21, No. 4, 1980.
16.
SATURN - Mini Document. SATURN 7.1 - Introductorv Users Manual. March 1987; Fundamental Requirements of Full Scale Dvnamic Route Guidance, January 1990.
17.
D. Van Vliet and al.. SATURN - A Simulation-Assignment Mode1 for the Evaluation of Traffic Management Schemes. Trafftc Engineering and Control, Vol. 21, No. 4, 1980.
18.
D. Van Vliet, SATURN: A Modern Assignment Model. Traffic Engineering & Control, Vol. 23, No. 12, 1982.
19.
Van Aerde, Dr. M., INTEGRATION. Demonstration Package, May 1990.
20.
Taylor, N.B., RODIN, User Guide, 1991.
21.
1985 Highwav Canacitv Manual. TRB Special Report 209, Washington D.C.
22.
May, A.D., Freewav Simulation Models - Revisted, TRB Freeway Operations Committee Meeting, January 1987.
23.
Breheret, L., Hounsell, N.B., McDonald, M., The Simulation of Route Guidance and Traffic Incidents, Transportation Research Group, University of Southampton, January 1990.
24.
Davidson, K.B., The Theoretical Basis of a Flow-Travel Time Relationship for Use in Transnortation Planning, Australian Road Research, Volume 9, No. 1, March 1978.
110
APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
APPENDIX E
~CONTINUOUS
TR
AF
F IC
AS S
I G N M E N T
M O D E L
V E R S I O N
TRANSPORT AND ROAD RESEARCH LABORATORY (DTP) CROUTHORNE, RGII 6AU, ENGLAND
5
CONTRAM 5 . 1 4 ( 1 6 . 4 . 9 1 )
RUN ON 11/ 6/91
= TRAFFIC GROUP, 0344-770494 = CROWN COPYRIGHT 1990
NETWORK AND TIME DATA SMART CORRIDOR BASE RUN NETWORK and TIME DATA (6/11/91) CARD
(TIME)
DEFINITION OF TIME SLICES IN SIMULATION PERIOD ( 24 HOUR CLOCK )
TYPE
(UNIT)
TIME SLICE NUMBER : 1 2 600
1
1
630
3
700
FEEDS
NUMBER
(LETTERS DENOTE BANNED MOVEMENTS)
3
5001
7
0
0
0
0
3
5002
5303
5304
158
128
0
3
5003
4803
4804
4603
157
0
3
5004
4607
4105
4106
0
0
3
5005
3506
5006
7607 3610
3505
3
4203 3603
4107
3005
0 3011
3
5007
3103
3110
3507
3508
2505
3
5008
2603
2610
3007
2005
2006
3
5009
2103
2110
2507
2508
1505
3
5010
1603
2007
2008
905
0
3
5011
1003
1010
1507
205
211
3
5012
80
82
83
0
0
3
5013
81
84
0
0
0
3
5014
50
0
0
0
0
3
5015
907
908
303
304
0
3 3
5016
209 309
703
704
0
5017
202 301
1307
1308
0
3
5018
8001
705
1807
1808
0
3
5019
1701
1305
1306
2307
0
3
5020
2201
2209
1805
1806
2807
3
5021
2701
2709
2305
2306
3307
3
5022
3201
2805
2806
3807
0
3
5023
3701
3709
3305
3306
4407
3
5024
4201
3805
3806
4907
0
3
5025
4405
4406
5307
4801
4809
3
5026
4905
128
0
0
0
NUMBER
TYPE
LINKS
5
6 830
ORIGIN
TO
5
800
TYPE
FEEDS UP
TO
730
5
CARD
UNCONTROLLED . . . LINK CARD SET
UP
4
7 900
8 930
9 1000
10 1300
0
LINKS
OR
CRUISE LENGTH
DESTINATIONS : (LETTERS DENOTE BANNED MOVEMENTS)
TIME (SECS)
SAT/N FLOW
STORE JUNCTION
CAP. (METRS) (PCU/H) (PCUS)
NUMBER
4
1
50
51
0
0
0
0
86V
732
10500
139
136
1
51
52
0
0
0
0
86V
387
10300
100
137
4
1
52
53
1101
0
0
0
86V
320
4
1
53
0
0
0
0
86V
335
12000 11400
4
1
54
_ 54 55
1607
0
0
0
86V
122
4
1
55
56
0
0
0
0
86V
518
4
1
56
57
2107
0
0
0
86V
290
4
1
57 58
58 59
0 2607
0 0
0 0
0
86V
10300
0
86V
488 183
59
60
0
0
0
0
86V
671
10600
4
1 1
12 0
13 0
:
4
4
11
10900 12500 11300 9800
96
138
95
139
33
140
162
141
82
142
126 45
143 144
178
145
% DELAY
0
4
1
60
61
3107
0
0
0
8&J
183
9900
45
146
4
1
61
62
0
0
0
0
86V
579
9450
137
147
4
1
62
63
3607
0
0
0
86V
899
11550
260
148
4 4
1
63
64
0
86V
655
9500
156
64
65
0 0
0
1
0 4207
0
0
86V
930
8800
205
149 150
4
1
65
66
0
0
0
0
86V
198
9100
45
151
4
1
66
67
0
0
0
0
80
101
9600
24
152
4
1
67
68
0
0
0
0
86V
472
9100
107
153
4
1
68
69
4601
0
0
0
8&J
899
9150
206
154
4
1
69
70
0
0
0
0
86V
290
9100
66
155
4
1
70
71
0
0
0
0
8&l
732
7600
139
156
4
1
71
72
0
0
0
0
86V
533
7550
101
157
4
1
72
9001
0
0
0
0
80
701
9500
166
158
4
1
7
a
5305
5311
0
0
86V
853
15000
469
107
9
0
0
0
86V
335
8700
3500
108
0
0 0
0
86V
122
9200
28
109
0
0
0
8&
152
8600
33
110
86V
457
9000
103
111
4
1
8
4
1
9
10
5205
4
1
10
11
4805
4
1
11
12
0
0
0
0
4
1
12
13
0
0
0
0
86V
518
9300
120
112
4
1
13
14
4305
0
0
0
86V
1204
8000
241
113
4
1
14
15
0
0
0
0
86V
183
8100
37
114
4
1
15
16
4205
0
0
0
86V
122
9000
27
115
4
1
16
17
0
0
0
0
86V
442
8500
94
116
4
1
17
18
3705
0
0
86V
671
9500
159
117
4
1
18
19
0
0
0
0 0
86V
701
9450
166
118
4
1
19
20
3205
0
0
0
86V
884
10900
241
119
4
1
20
21
0
0
0
9500
1
21
22
2705
0
0
86V 86V
366
4
0 0
320
10100
87 81
121 122
120
4
1
22
23
0
0
0
0
86V
655
12100
198
4
1
23
24
2205
0
0
0
86V
335
12100
101
123
4
1
24
25
0
0
0
0
86V
457
10300
118
124
4
1
25
26
1705
0
0
0
86V
305
11900
91
125
4
1
26
27
0
0
0
0
86V
579
12500
181
126
4
1
27
28
1205
0
0
0
86V
213
11350
60
127 128
4
1
28
29
0
0
0
0
86V
472
8500
120
4
1
29
30
85
0
0
0
86V
213
9850
52
129
4
1
30
31
86
0
0
0
86V
290
9000
65
130
4
1
31
32
0
0
0
0
8&l
274
7500
51
131
4
1
32
9014
0
0
0
0
86V
213
50
132
4
1
80
9013
0
0
0
0
86V
700
9300 7600
462E
160
4
1
81
9012
0
0
0
0
86V
700
7600
462E
161
CRUISE
LENGTH
SAT/N
STORE
TO
SIGNALISED . . . . .
FEEDS UP
CARD SET
DESTINATIONS :
LINK NUMBER
TYPE
5
LINKS
OR
TIME
(LETTERS DENOTE BANNED MOVEMENTS)
SIGNAL/ STAGES % GREEN % DELAY
CAP. JUNCTION
(METRS) (PCU/H) (PCUS)
UHEN
NUMBER
GREEN
6
2
705
301
9017
0
0
55v
837
2778
202E
7
1
6
2
1305
705
0
0
0
0
55v
732
5100
324E
13
1
6
2
8001
0
0
0
0
55v
732
900
6
2
1306 1307
0
0
0
55v
837
13 13
1 1
6
2
1308
9018
5100 1000
57E 371E
6
2
1805
1305
6
2
1806
l-701
6
2
1807
2307
6
2
1808
9019
6
2
2305
1805
6
2
2306
6
2
6
2
1807
309
(SECS)
FLOW
1808
0
0
0
55v
837
72E
13
1
1306
0
9019
0
0
55v
804
237E
18
1
0
0
0
0
55v
804
62E
18
1
2308
0
0
0
55v
719
212E
18
1
0
0
0
55v
719
31E
18
1
1806
9020
0
0
55v
815
240E
23
1
2201
2209
0
0
0
55v
815
62E
23
1
2307
2807
2808
0
0
0
55v
658
194E
23
1
2308
9020
0
0
0
0
55v
658
38E
23
1
0
675
2305
2306
0
0
0
55v
802
3400
237E
28
2
2
2805 2806
2701
2709
0
0
0
55v
802
700
48~
28
2
6
2
2807
3307
3308
0
0
0
55v
815
3400
240E
28
2
6
2
2808
9021
0
0
0
55v
815
3305
2805
9022
0
0
55v
1591
42E 470E
2
2
600 3400
28
6
0 2806
33
1
6
2
3306
3201
0
0
0
0
55v
1591
955
132E
33
1
6
2
3307
3807
3808
0
0
0
55v
802
3400
237E
33
1
6
2
3308
0
0
0
0
55v
802
1140
79E
33
1
6
2
3805
9022 3305
3306
0
0
0
55v
1478
3400
436E
38
2
6
2
0
0
0
55v
1478
600
77E
38
2
2
3701 4407
3709
6
3806 3807
4408
0
0
0
55v
1591
3400
470E
38
2
6
2
3808
9023
0
0
0
0
55v
1591
825
114E
38
2
6
2
4405
3805
3806
9024
0
0
55v
1829
3400
540E
44
2
6
2
6
6
2
4406
4201
0
0
0
0
55v
1829
600
95E
44
2
6
2
0
55v
1478
3400
436~
44
2
0
0 0
0
55v
1478
5308
0
0
55v
1829
125E 540E
2
5307
975 3400
44
4907
0 0
0
6
2 2
4907 9024
4908
6
4407 4408
49
2
6
2
0
0
0
55v
1829
1155
183E
49
2
3
9025 9015
0
6
4908 205
0
0
0
0
55v
1272
5100
564E
2
1
6
3
206
9015
0
0
0
0
55v
1272
500
55E
2
1
6
3
211
303
304
0
0
0
55v
1272
1450
160E
2
1
6
3
905
206
205
0
55v
692
5100
306E
9
1
3
906
9011
0
211 0
0
6
0
0
55v
692
825
49E
9
1
6
3
907
1507
1508
0
0
0
55v
1201
5100
532E
9
1
6
3
908
1003
1010
0
0
0
55v
1201
1600
l67E
9
1
6
3
1505
906
905
0
0
0
55v
814
3400
240E
15
2
6
3
1506
9010
0
0
0
0
55v
814
800
56E
15
2
6
1507
2007
2008
0
0
0
55v
692
3400
204E
15
2
6
3 3
1508
1603
0
0
0
0
55v
692
1600
96E
15
2
6
3
2005
1505
1506
0
0
0
55v
796
3400
235E
20
1
0
0
55v
796
800
55E
20
1 1
6
3
2006
9009
0
0
6
3
2007
2507
2508
9009
0
0
55v
814
3400
240E
20
6
3
2008
2103
2110
0
0
0
55v
814
500
35E
20
1
6 6
3
2505
2006
2005
0
0
0
55v
805
3400
238~
25
2
3
2506
9008
0
0
0
0
55v
805
650
45E
25
2
6
3
3007
9008
0
0
0
55v
796
3400
235E
25
2
6
3
2507 2508
2603
2610
0
0
0
55v
796
500
34E
25
2
6
3
2506
2505
0
0
0
55v
1234
3400
364E
30
1
6
3
3005 3006
9007
0
0
0
0
55v
1234
536
57E
30
1
6
3007 3011
3507 3103
3508
0
56V
805
2600
182E
0
0
55v
1234
1450
155E
30 30
1
3110
0 0
0
6
3 3
6
3
3505
3005
3006
0
0
55v
1526
5100
676E
35
2 2
1
6
3
3506
9006
0
3011 0
0
0
55v
1526
500
66E
35
6
3
3507
4107
4108
9006
0
0
55v
1164
5100
516E
35
2
6
3
3508
3603
3610
0
0
0
55v
1164
625
63E
35
2
6
3 3
4105
3506
3505
0
0
0
55v
1280
3400
378~
41
3
6
4106
9005
0
0
0
0
55v
1280
500
55E
41
3
6
3
4107
7612
7607
0
0
0
55v
1303
3400
385E
41
3
6
3
4108
4203
0
0
0
0
55v
1303
875
99E
41
3
6
3
7606
7605
0
0
0
55v
227
5100
100E
46
1
6
3
4605 4606
4707
0
0
0
0
55v
227
575
11E
46
1
6
3
4607
4707
4807
4808
0
0
55v
792
5100
351E
46
1
6
3
4805
4903
4904
0
55v
300
3400
48
2
3
4807
4701
4702
4910 0
4601
6
0
0
55v
213
3400
62~
48
2
6
3
4808
4903
4904
4910
0
0
55v
213
1600
29E
48
2
6
3
7605
4106
4105
9004
0
0
55v
792
5100
351E
76
1
6 6
3
7606
9004
0
0
0
0
55v
792
740
76
1
3
7607
4607
0
0
0
55v
1280
5100
76
1
6
3
7612
9004
0 0
50E 567~
0
0
0
55v
1280
1450
161E
76
1
6
4
1501
905
906
9010
0
0
55v
280
3400
82E
15
1
2000
6
4
1502
6
4
1601
1501
2008
0
0
0
55v
280
500
12E
15
1502
0
0
0
55v
50
5100
22E
16
1
6
4
1602
151
0
0
0
0
55v
50
3200
13E
16
6
4
1603
1703
1704
151
0
0
55v
280
5100
124E
16
1
6
4
1607
1501
1502
1703
.1704
151
55v
250
3400
16
3
6
4
1701
1601
1602
121
0
0
55v
1330
5100
589E
17
0
0
0
55v
165
5100
73E
17
1
0
0
0
55v
165
1600
22E
17
2
2000
2
6
4
1703
1803
1804
6
4
1704
121
0
6
4
1705
1803
1804
1601
1602
121
55v
250
3400
17
3
0
0
55v
1330
3400
393E
18
2
2000
6
4
1803
2307
2308
9019
6
4
1804
1305
1306
0
0
0
55v
1330
500
57E
18
2
6
4
2001
9009
0
0
0
0
55v
860
3400
254E
20
2
6
4
2002
2507
' 2508
0
0
0
55v
860
500
37E
20
2
6
4
2009
1505
1506
0
0
0
55v
860
1450
108E
20
2
6
4
2101
2001
2002
2009
0
0
55v
360
3400
106E
21
2
6
4
2102
152
0
0
0
0
55v
360
1600
50E
21
1
6
4
2103
2203
2204
0
0
0
55v
860
3400
254E
21
2
6
4
2107
2203
2204
2001
2002
2009
55v
240
3400
21
3
6
4
2110
152
0
0
0
0
55v
860
1450
6
4
2201
2101
2102
0
0
0
55v
1390
3400
6
4
2203
2303
2304
0
0
0
55v
360
3400
6
4
2204
122
0
0
0
0
55v
360
6
4
2205
2303
2304
2101
2102
122
55v
240
6
4
2209
122
0
0
0
0
55v
1390
1450
6
4
2303
2807
2808
9020
0
0
55v
1390
3400
6
4
2304
1805
1806
0
0
0
55v
1390
500
6
4
2501
2005
2006
9008
0
0
55v
820
6
4
2502
3007
0
0
0
0
55v
2000
21
2
4lOE
22
2
106E
22
2
56E
22
1
22
3
175E
22
2
410E
23
2
60E
23
2
3400
242E
25
1
820
500
35E
25
3400
2000
6
4
2601
2501
2502
0
0
0
55v
400
3400
118E
26
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4
2602
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0
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400
1600
55E
26
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2603
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55v
820
3400
242E
26
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820
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5~
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26~ 2000
2 2
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212 200
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169
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220
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298
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13
2
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298
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298 97
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1
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3400 3400
80
6
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152 140
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157
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152
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213
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48
4
6
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6
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127
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140
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213
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213
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1
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500 3235
1
4801
9E 120E
49
6
0 0
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6
4904 4905
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213
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49
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6
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0
0
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210
5100
93E
52
3
29E
52
3
52
3
6
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0
0
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210
1600
6
6
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9002
0
0
0
0
55v
500
3400
6
6
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4.905
9026
0
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210
5100
93E
53
1
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128
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210
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9E
53
1
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6
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189E
53
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334
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6
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4
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457
5100
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457
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334
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334
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334
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300
9000
234E
136
8
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500
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200
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126
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250 250
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8 8
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124
123
23
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300
3400
88E
122
6 6
3
160 161
8
124
21
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250
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73E
120
6
8
125
19
0
0
0
0
5ov
300
3400
88E
118
6
8
126
17
0
0
0
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5ov
300
3400
88E
116
6
8
127
13
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0
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0
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400
3400
118E
112
6
8
128
9
0
0
0
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5ov
400
3400
118E
108
6
8
150
54
0
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200
3400
59E
139
6
8
151
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141
6
8
152
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0
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250
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73E
143
6
8
153
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0
0
0
5ov
300
3400
88E
145
6
8
154
62
0 0
0
0
0
5ov
250
3400
73E
147
1
6
8
155
64
0
0
0
0
5ov
300
3400
88E
149
1
6
8
156
68
0
0
0
0
5ov
300
3400
88E
153
6
8
157
71
0
0
0
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5ov
350
3400
103E
156
6
8
158
72
0
0
0
0
5ov
300
3400
88E
157
OFLACS:-
V = SPEED IN KM/H,
CARD
VEH
TYPE
CLASS
SF = LANES*1000 + SPEED/FLOW NUMBER,
FUEL COEFFICIENTS
(ML/M)
B
0.361
C 6
0.024 -0.040
2.272
D/F
L
-0.040
2.272
D/F
1.0
C
El
(ML/KJ..)
CT)
0.000057
E2
0.025
0.000334
1.080 8.000
0.087 0.074
0.025
0.000334
5.000
0.074
0.025
CRUISE TIMES (% CAR VALUE)
PCUS PER CLASS CAR
EFFICIENCY
M
( M L / S ) (ML/MV**2)
D/F D/F
TYPE
WEIGHT
CRUISE A
CARD
E = ESTIMATED STORAGE CAPACITY,
B
L
CAR
B
L
2.0
1.5
100
100
100
NETWORK COMPOSITION DEDUCED FROM DATA: 57 0 218 41
1
UNCONTROLLED LINKS GIVE-WAY LINKS
)
SIGNALISED LINKS SIGNAL JUNCTIONS
NO. OF ORIGINS
TOTAL OF
275 LINKS OF ALL TYPES
1 TOTAL OF 26
92 JUNCTIONS OF ALL TYPES
NO. OF DESTINATIONS
26
1
1 D = DEPARTURES,
A = ARRIVAL
FLOWS
ORIGINS
5001
2738
7242
9006
9006
6836
5368
3628
2738
0
5002
62
150
190
190
130
116
78
62
0
5003
644
1666
2086
1640
1250
844
644
0
5004
136
348.
336
254
180
136
0
5005
300
782
962
962
766
594
398
300
0
1796
1380
1048
726
556
0
a4
66
52
0
438
438
5006
556
1446
1796
5007
52
134
162
162
118
5008
320
816
1018
1018
762
598
412
320
0
5009
168
428
528
528
402
304
216
168
0
5010
324
822
1024
1024
742
552
420
324
0
5011
12
30
32
32
26
18
14
12
0
778
2048
2554
2554
1948
1430
1026
778
0
5013
778
2048
2554
2554
1992
1332
1026
778
0
5014
1808
4844
5990
6028
4810
3232
2376
1808
0
5015
28
80
92
92
80
58
32
28
0
5016
38
86
106
106
ah
58
46
38
0
5017
a
18
18
18
18
12
8
a
0
5018
108
286
346
346
286
186
142
108
0
166
204
204
160
120
86 --
70
0
5020
114
280
346
346
276
206
142
114
0
5021
214
532
662
662
510
390
270
214
0
5022
34
90
110
110
78
68
44
34
0
5023
354
944
1170
1170
916
656
476
366
0
5024
324
862
1066
1066
726
630
432
324
0
5025
358
914
1138
834
654
464
358
0
5026
214
540
1138 670
670
496
400
270
214
0
5012
'
-
5019
.-. 72
OTOTAL VEHICLE FLOW RATES DIRECTED TOWARDS EACH DESTINATION DURING EACH TIME SLICE (VEH/H) FLOWS
DESTINATIONS
2996
9001
OTOTAL
9884
9884
7612
5120
3970
2996
0
5888
4356
3498
2362
1792
0
9014
1792
4728
5888
9013
536
1398
1736
1736
1238
1042
684
536
0
9012
1160
3048
3804
3804
2896
2282
1512
1160
0
9017
146
364
460
460
354
248
178
146
0
9018
78
194
234
234
188
146
98
78
0
9019
46
122
142
142
112
a0
56
46
0
9020
50
152
182
182
148
118
68
50
0
9021
112
266
328
328
264
198
142
112
0
9022
26
58
64
64
50
40
34
26
0
9023
218
592
700
738
578
422
300
218
0 0
9024
132
324
400
400
324
244
162
132
9025
48
140
170
170
132
104
66
48
0
9015
162
394
498
498
370
304
210
162
0
9011
186
470
588
588
442
352
244
186
0
9010
564
1486
1844
1844
1472
1058
748
564
0
9009
262
666
830
830
658
500
334
262
0
9008
90
236
284
284
234
176
116
90
0
9007
244
628
784
784
620
476
316
244
0
9006
228
274
a6
0
1042
228 810
120
842
274 1042
178
9005
86 328
622
426
328
0
9004
430
1120
1390
1390
1120
832
560
428
0 0
9003
340
890
1108
1108
852
604
448
346
9002.u
416
1078
1346
1346
1072
800
544
422
0
9026
50
134
164
164
126
104
64
50
0
9016
44
110
124
124
98
70
54
44
0
13816
10552
0
VEHICLE FLOW RATES ENTERING THE NETWORK DURING EACH TIME SLICE (VEH/H)
10542 ONUMBER
7934
27602
OF ORIGIN-DESTINATION (O-D) CARDS
LAST TIME SLICE WITH NON-ZERO O-D DEMAND L E N G T H O F “BUSY P E R I O D ” I N M I N U T E S ESTIMATED TOTAL DEMAND (VEHICLES)
34268 2420 8 240
88529
34306
26354
19618
- MEAN TRAVEL TIMES PER VEHICLE (SEC)
CINK-BY-LINK ALL-TIME-SLICES
1
RUN ON ll/ 6191
SMART CORRIDOR BASE RUN NETWORK and TIME DATA (6/11/91) SMART CORRIDOR BASE DEMAND SMART
CONTROL
CORRIDOR
DATA ITERATION
TIME
SLICES
1
LINK
NUMBER
: 3
2
5
4
7
6
8
9
NO.&
MEAN
TYPE
QUEUE TIME
600
630
700
830
800
730
900
930
1000
1300
7u
35
35
35
35
35
35
35
35
0
35
84l
14
14
15
33
31
14
14
14
0
20
14
8
5
5
5
0
7
9u
5
5
7
IOU
6
6
7
10
8
7
6
6
0
7
IlU
19
19
24
35
27
21
19
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29
32
35
33
33
35
31
30
28
33
5202s
30
37
176
178
480
75
39
30
0
188
5205s
47
48
49
49
49
48
47
47
0
48
5303s
25
25
25
25
25
25
25
25
0
25
5304s
0
0
0
0
0
0
0
0
0
0
5305s
34
35
42
103
47
35
35
34
0
65
39
111
5307s
41
43
55
144
267
102
5308s
40
41
41
41
41
41
42 40
41 40
39
41
5311s
34
35
35
35
35
35
34
34
0
35
7605s
59
60
62
63
61
60
59
59
58
61
59
61
62
60
58 90
60
92
58 90
0
92
59 91
0
91
98
100
95
91
91
0
96
10
9
10
7606s
58
7607s
90
91
91
7612s
91
92
94
7801s
9
10
10
11
11
9
0
78035
13
13
13
13
13
13
13
12
0
13
8001s
22
22
22
23
23
22
22
22
0
22
8003s
9
9
9
9
9
9
9
9
9
1
LINK-BY-LINK
TOTAL DELAY (VEH-H)
ALL-TIME-SLICES
9 RUN ON II/ 6/91
SMART CORRIDOR BASE RUN NETWORK and TIME DATA (6/11/91) SMART CORRIDOR BASE DEMAND SMART CORRIDOR
CONTROL DATA I T E R A T I O N NUMSER 3
TIME SLICES : 1
LINK
3
2
4
6
5
7
8
9
NO.&
TOTAL
TYPE
DELAY 600
630
730
700
800
900
830
930
1000
1300
7u
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.85
9.73
25.97
.oo
.oo
.oo .oo
.oo
8u
-00
36.55 14.35
w
.oo
.oo
1.63
9.09
3.57
.07
.oo
.oo
.oo
1ou
.oo
.oo
1.50
4.56
1.73
1.02
.oo
.oo
.oo
8.81
IIU
-00
.oo
3.99
14.81
8.31
2.19
.oo
-00
.oo
29.30
12u
.oo
.oo
5.80
18.07
12.04
4.06
.oo
.oo
.oo
39.97
13u
.oo
.Ol
61.40
134.69
126.31
20.87
.oo
.oo
.oo
343.29 43.90
14u
.oo
-00
3.52
17.86
22.53
.oo
-00
-00
.oo
15u
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
16U
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
-00
17u
.oo
.oo
.oo
-00
.oo
.oo
.DO
.oo
.oo
.oo
.oo
.oo
.OD
.oo
.oo .DO
.oo
-00
.oo .oo
.oo .oo
-00 .oo
.oo
.oo
.oo .oo
-00
2ou
.oo .oo
.oo .oo
.oo
19U
.oo -00
.oo .oo
.oo
18U
21u
.oo
.oo
19.53
1.10
1.15
.32
.oo
.oo
.oo
22.11
22u
.oo
.oo
-00
4.08
6.06
4.97
.oo
-00
-00
15.12
6.10
5.66
1.59
.oo
.oo
.oo
13.35
6.89
11.53
4.36
.oo
.oo
.oo
22.94
23U
.oo
.oo
.oo
24U
-00
-00
.17
.oo
25U
.oo
_* 00
1.59
13.38
13.75
4.15
.oo
-00
.oo
32.86
26U
.oo
-00
5.97
24.02
23.07
6.46
.oo
.oo
.oo
59.51
27U
.oo
.oo
2.24
5.36
13.09
4.45
.61
-00
.oo
25.75
28U
.oo
.oo
31.12
76.34
78.63
2.27
.oo
.oo
254.04
2w 3ou
.oo .oo
.oo .oo
.oo -00
.oo .oo
.oo
65.69 .oo .oo
.oo -00
.oo .oo
3lU
-00
.oo
.oo
.oo
.oo
.oo
-00
-00
.oo
.oo
.oo
.oo -00
.oo .oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo -00
.oo .oo
.oo
.oo
.oo
.oo
.oo
-00
.oo
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
-00
-00
.oo
-00
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.22
2.44
2.30
.oo
.oo
.oo
-00
4.96
.oo
13.48
25.00
1.52
.oo
.oo
-00
.oo
39.99
.oo .oo
-08
1.25
.81
.oo
.oo
.oo
.oo
2.15
10.86
25.98
.89
.oo
.oo
.oo
.oo
37.73
32U
.oo
-00
5ou
-00
.oo
51u
.oo
.oo
.oo
52U
-00
53u
-00
.oo .oo
-00 .oo
54u
.oo
.oo
.oo
55u
.oo
.oo
-00
56U
.oo
.oo
-00
57U
.oo
.oo
58U
-00
59U
-00
60U
.oo
61U
.oo
62IJ
.oo
.oo .oo
.oo -00
6.04
9.38
7.48
.oo
.oo
-00
-00
22.91
-59
14.01
16.25
3.48
.oo
.oo
.oo
34.34
63U
.oo
.oo
3.72
16.34
12.18
4.95
.oo
.oo
-00
37.19
64U 65U
.oo
.oo
99.72
59.50
14.40
.oo
-00
.oo
-00
5.24
.31
-00
.oo
.oo -00
226.96
.oo
53.34 -00
66U
-00
.oo
.oo
.oo
7.30
.68
.oo
.oo
.oo
7.98
67U
.oo
.oo
.oo
9.76
13.88
2.79
.oo
.oo
-00
26.43
68U
.oo
.oo
1.12
19.93
55.14
28.13
.oo
.oo
.oo
104.32
69U
.oo
.oo
1.08
7.51
10.80
.54
.oo
.oo
-00
19.93
7ou
.oo
.oo
4.37
28.51
40.27
22.34
2.99
.oo
-00
98.48 264.57
5.56
71u
.oo
.oo
30.96
72.63
83.10
74.82
3.07
.oo
.oo
72U
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo
-00
-00
.oo
-00
.oo
.oo .oo
.oo
80U
.oo .oo
-00
.oo
81U
.oo
.oo
.oo
.oo
-00
.oo
-00
.oo
.oo
.oo
82U
.oo
.oo
.oo
-00
.oo
-00
.oo
.oo
.oo
83U
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
84U
.oo
.oo .oo
.oo .oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
85U
.oo
.oo
.oo
.oo
.oo
.oo
-00
.oo
.oo
.oo
86u
.oo
.oo
.oo
-00
-00
.oo
.oo
.oo
.oo
87U
.oo
.oo .oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
120s
.oo
.oo
-00
-00
-00
.oo
.oo
.oo
-00
-00
121s
.oo
.oo
-39
2.27
2.43
.33
.oo
.oo
.oo
5.43
122s
.oo
.oo
.I0
2.05
3.92
1.14
.oo
-00
.oo
7.21
123s
.oo
.oo
.oo
.47
1.04
.I3
.oo
.oo
-00
1.64
124s
.oo
-00
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
125s
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
126s
.oo
.oo
.oo
.oo
-00
-00
.oo
127s
-00
.oo .oo
.oo .oo
.oo
.32
.98
.24
.oo
.oo
.oo
1.54
128s
.oo
.39
4.12
4.94
.oo
.oo
.oo
.oo
9.45
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
150s
.oo
.oo .oo
151s
.oo
.oo
.oo
.oo
.oo
.a0
-00
.oo
.oo
.oo
152s
.oo
-00
.oo
.oo
.oo
.oo
.oo
.oo
.oo
.oo
153s
.oo
-00
.oo
.08
.04
.oo
.oo
-00
.oo
.12
154s
-00
.oo
-00
.36
.17
.oo
.oo
.oo
.oo
-52
155s
.oo
.oo
1.14
3.00
2.30
-69
.oo
.oo
.oo
7.13
156s
.oo
.oo
.oo
2.25
6.69
2.60
.oo
.oo
-00
11.54
157s
5.08
28.29 .oo
39.97 .oo
19.09 .oo
1.14 -00
.oo
.oo
-00
-00 .oo
93.57 .oo
158s 202s
.oo .oo
.oo .oo
.05
-11
.12
-76
.78
.09
-06
.05
.oo
2.01
205s
.I7
.53
.63
-44
.40
.28
-19
.02
3.01
206s
.oo
.02
.oo
.oo
.oo
.oo
.oo
.03
209s
.oo -02
.35 .Ol ,04
.07
.06
.04
.04
-04
-02
.oo
.31
211s
-04
.I5
.24
.63
.53
.I4
.07
.06
.oo
1.86
301s
.Ol
.03
.05
.34
.32
-03
.02
.Ol
.oo
.82
303s
.02
.06
.09
.18
.I7
-06
.03
.02
.oo
.64
304s
.Ol
.03
.03
.07
-07
.Ol
-01
.Ol
-00
.25
309s
.02
.05
.05
.04
.03
-04
.03
.02
-01
.28
703s
-03
.09
.I5
.26
.24
.lO
.05
.04
.oo
.96
704s
.oo
.oo
.oo
.oo
.Ol
.oo
-00
.oo
.oo
.Ol
705s
.24
-60
-91
2.52
1.92
.51
.35
.27
-03
7.35
901s
-19
-42
.45
.38
.26
-38
.29
2.59
.oo
.Ol
.03
-08
.02
.Ol
.oo
.21 -00
-02
902s 905s
.07
.22
.38
-80
-67
.29
-10
-08
.oo -00
2.61
906s
.03
-18
.42
1.34
2.40
.28
.06
-03
.oo
907s
.Ol
.03
-04
.05
.06
.02
-01
.Ol
.oo
4.73 -21
908s
.08
.I9
-20
1.07
1.62
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.09
-08
.oo
3.60
909s
.16
.37
.34
-34
-23
.25
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.I8
.02
2.15
1001s
.14
.40
.43
-40
-24
.34
.24
.17
.03
2.40
1002s
.24
-843
1.01
.93
1.06
.83
.33
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.oo
5.53
1003s
.02
.05
.06
-37
-51
.20
.02
-02
.oo
1.25
1005s
1.43
5.58
9.42
10.72
4.88
3.08
1.91
1.42
-00
38.44
.15
1010s
.05
.I2
.12
.09
.08
.06
.05
-05
.oo
-62
1101s
.76
1.21
1.13
1.18
.88
.81
.74
-00
7.69
1108s
.06
.06
.oo
2.27
2.29
.16 1.17
.07
.30
.23 1.69
.18
1201s
.18 .83
.98 .21
.39
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1.15 9.23
1203s
.I4
-20
.18
.25
.24
.18
.18
.14
-00 .oo
1204s
.I4
.20
-25
1.19
5.16
2.31
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.13
.oo
9.44
1205s
.57
1.71
2.37
1.32
.99
1.76
.89
.61
-05
10.28
1303s
.lO
.22
.23
.19
.17
-21
.12
-11
.oo
1.34
1304s
.I8
.46
.72
.37
.23
.33
.26
.19
.02
2.75
1305s
-04
.I1
.20
-81
-80
.12
.05
.04
.oo
2.18
13065
.03
.08
4.60
8.84
10.00
.92
.03
-03
-00
24.52
1307s
.Ol
.03
-04
.06
-03
.02
.Ol
.Ol
-00
.19
.oo
.oo
.oo
.23
1.51
1308s
.oo
.Ol
.02
.08
.lO
.Ol
1310s
.oo
.oo
.05
-03
-04
.Ol
.oo
.oo
-00
.13
1501s
-36
1.61
2.15
2.59
2.30
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12.02
1502s
.oo
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.02
-03
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1.78 .Ol
.oo
.oo
.oo
.07
1505s
.ll
-38
1.16
3.47
2.46
-60
.I7
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8.49
1506s
.21
.91
6.27
11.66
17.17
-24
.oo
43.51
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.05
.I0
.20
.13
6.54 .04
.51
1507s
.02
.Ol
-00
.56
15085
.oo
.Ol
-01
.oo
.oo
.Ol
.oo
.oo
.oo
.03
1601s
.43
1.10
1.55
1.94
1.48
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9.16
1602s
.07
.I2
.12
.ll
.ll
1.33 .09
.07
.06
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1603s
.49
1.48
1.70
1.60
1.11
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8.41
1607s
.65
8.95
15.36
28.99
25.62
9.49
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2.38
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90.71
1701s
.07
.24
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.61
-43
.08
-07
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2.67
1703s
.03
.08
.I9
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.18
.04
.03
1704s
.22
1 .Ol
-68
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.07 -51
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3.92
.81
1705s
-95
6.20
13.62
24.76
-37 29.30
2.29
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87.68
1803s
.03
.04
.04
.03
.02
9.00 .03
.23 1.45
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18045
.oo
.oo
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.ll
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.oo
.oo
.oo
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1805s
.07
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1.51
4.26
4.85
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11.75
1806s
.Ol
.16
1.23
4.75
9.18
4.06
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.oo
19.42
1807s
.02
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.I3
.I4
.08
.04
.02
-02
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1808s
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-12
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2001s
.I7
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-19
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3.07
2002s
.oo
-01
.02
.03
-03
-00
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.oo
.oo
.09
2005s
.19
.60
1.05
1.50
1.42
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5.86
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1.48
1.67
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-07
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4.92
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1.46
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.oo
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1.31
2006s
.07
2007s
.04
2008s
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2009s
.oo
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.69
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2101s
-13
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2.98
2102s
.I5
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2.61
2103s
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-26
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1.61
2107s
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1.19
4.04
4.58
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13.85
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2.42
2303s
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2304s
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1.32
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2.59
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1.92
5.56
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2.55
1.67
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13.95
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2505s
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1.47
2.28
2.24
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8.32
4.30
2506s
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2507s
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2.11
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2601s
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1.28
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1 .Ol
.52
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4.60
2603s
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-42
.64
.69
.35
-31
.21
.15
.oo
2.93
2607s
.I7
.46
.78
2.22
.71
.31
-22
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.oo
5.04
2610s
-26
1.16
1.13
1.00
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.63
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.oo
5.64
2701s
.20
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-62
-43
.31
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3.01
2703s
-08
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-44
1.20
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-08
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2.75
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3.44
27045
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.82
2705s
.26
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1.15
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4.60
2709s
.I3
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1.06
1.35
1.47
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5.12
28035
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1.99
28048
.oo
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2805s
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1.90
1.69
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4.93
28065
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1.16
1.26
2.50
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5.09
28076
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2808s
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2810s
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-00
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3001s
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1.40
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5.49
3005s
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1.85
3.25
1.26 2.83
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10.71
3006s
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2.46
2.39
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6.55
3007s
.I0
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1.20
3.53
1.81
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7.42
3011s
.69
2.65
11.04
14.44
8.73
1.72
1.12
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.oo
41.04
3101s
.I4
-35
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2.44
3102s
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1.13
3103s
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1.05
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1.02
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6.01
3107s
.39
1.09
1.83
6.34
4.74
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.oo
16.20
3110s
.04
-12
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-07
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-06
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3201s
.07
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.69
.76
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4.07
32033
.oo
.oo
.oo
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.Ol
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3204s
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1.51
2.28
1.42
1.64
1.20
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9.73
32053
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.68
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3.06
3303s
-01
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3305s
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2.34
8.00
3.57
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14.77
3306s
.07
2.11
2.96
5.23
4.35
1.73
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16.88
3307s
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.I3
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-75
-38
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1.96
33088
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3501s
-01
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.Ol
.oo
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1.09
2.20
1.39
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6.43
3502s
.oo
3505s
.I1
3506s
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-09
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1.24
3507s
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1.11
2.25
2.18
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6.94
3508s
.oo
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.oo
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3601s
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.I4
.I4
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36025
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1.12
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4.70
3704s
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1.37
5.20
6.39
5.00
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3705s
.24
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.72
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-71
-56
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4.12
3709s
.91
7.76
11.29
10.62
2.30
4.12
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39.14
3803s
-16
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.77
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1.25 .25
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3.82
3804s
.oo
.Ol
.08
-41
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-60
38055
.I2
1.27
4.58
18.97
3.78
1.13
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.oo
30.26
3806s
1.72
1.85
2.00
4.74
4.02
1.65
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19.56
3807s
.I3
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-61
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-25
1.65 .05
1.93
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2.09
3808s
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.71
4101s
.24
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1.34
1.50
1.29
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7.02
4102s
.oo
.oo
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-92
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3.79
1.73
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.oo
1.40
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.oo .oo
1.29 8.35
3603s
-04
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-53
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3607s
-19
-60
1.96
5.10
2.58
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-36
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3610s
.29
1.16
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1.77
1.61
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-40
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3701s
-10
.28
.31
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.I4
.lO
3703s
.I0
.37
.77
1.16
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-85
2.61 11.49 6.72 1.66 3.86 19.17
4105s
-10
.58
4106s
.72
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.97
2.18
2.87
1.25
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-65
.Ol
9.59
4107s
.25
-68
3.40
10.49
16.21
2.54
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34.17
4108s
-00
.oo
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4.77
1.63
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.oo
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6.57
4201s
-11
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-30
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1.30
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-13
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3.33
42033
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1.43
1.85
1.05
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4205s
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1.27 .69
.57
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3.40
4207s
.39
1.42
2.79
5.43
3.93
1.63
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16.75
4303s
-01
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.oo
-57
4305s
.15
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1.34
4403s
.14
-36
-64
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3.06
4404s
.oo
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4405s
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.13
1.09
1.56
1.20
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-00
4.12
4406s
.oo
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4.08
5.18
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9.32
4407s
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.I6
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-31
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3.13
7.48
4408s
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.oo
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-25
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4601s
1.44
8.27
11.13
16.41
4.67
5.24
2.30
1.91
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51.47
46036
.I0
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1.55
2.69
1.11
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6.89
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4605s
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4606s
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4607s
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1.35
2.45
3.12
3.74
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13.76
4701s
.I0
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1.49
3.08
2.12
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8.72
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1.23
2.44
2.91
1.87
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9.06
4707s
.95
3.13
4.86
5.93
7.60
5.57
1.35
1.15
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30.61
4801s
.21
1.08
2.64
5.84
3.09
1.53
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15.22
4803s
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1.39
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1.05
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5.20
48048
1.33
11.52
18.00
21.87
8.50
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4.58
1.39
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75.31
4805s
-44
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1.24
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4.54
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6.57
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16.62 40.62
4807s
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2.25
3.03
4808s
.61
2.73
2.52
2.40
3.49
2.44
1.50
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-62
7.62
10.98
13.38
4.68
1.82
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4.60
4903s
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.I5
1.31
1.69
1.41
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4905s
.20
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1.88
3.47
1.85
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9.20
4907s
-06
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1.23
2.22
1.85
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6.70
4908s
.oo
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4910s
.36
2.01
4.84
1.87
2.71
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17.31
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.72
2_.04
2.90
2.31
2.41
2.97
1.43
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15.78
5202s
.22
-80
9.07
12.33
33.74
3.45
1.23
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61.06
5205s
.ll
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-60
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2.93
5303s
-01
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.oo
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5304s 5305s
.04 .oo
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2.17
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18.33
5307s
.75
2.12
5.30
16.71
40.89
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1.47
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93.33
.oo
53083
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-08
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5311s
-02
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7605s
.50
1.17
2.11
2.71
1.56
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-67
-60
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10.29
7606s
-02
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.I4
.I9
.I0
-06
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.60
7607s
.ll
.38
-75
.95
'. 1 . 0 5
-38
.I4
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3.88
76129
.08
-22
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1.09
1.15
-59
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.09
.oo
3.84
7801s
.05
.I7
-34
.45
.52
.22
.07
.05
.oo
1.87
7803s
.12
.22
.28
.20
.I8
-20
.I5
-08
.oo
1.43
8001s
.05
.I3
-25
.42
.38
.I7
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1.51
80035
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.I5
.I3
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.09
-00
OTOTALS
33.82
65.44 37.44 1.15 140.33 543.26 1181.42 1144.84 465.16 LINK-BY,-LINK ALL-TIME-SLICES - AVERAGE SPEED OF A CAR (KM/H)
1.20
1.09 3612.86 RUN ON ll/ 6/91
SMART CORRIDOR BASE RUN NETWORK and TIME DATA (6/11/91) SMART CORRIDOR BASE DEMAND SMART CORRIDOR
CONTROL DATA ITERATION NUMBER 3
TIME SLICES : LINK
1
3
2
4
5
6
7
8
9
NO.&
OVERALL
TYPE
AVERAGE SPEED 600
630
700
800
730
830
900
7u
87.7
87.7
87.7
87.7
87.7
8u
86.1
86.1
81.7
51.4
28.9
87.7 86.1
930 87.7 86.1
1000
1300
87.7
.O
86.1
.O
87.7 60.1 60.8
9u
87.8
87.8
68.9
33.3
52.9
86.4
87.8
87.8
.O
1ou
91.2
91.2
74.7
53.2
71.8
74.4
91.2
91.2
.O
73.9
11u
86.6
86.6
72.5
48.5
60.3
75.1
86.6
86.6
.O
68.9
12u
88.8
88.8
70.4
47.4
56.4
71.3
88.8
88.8
88.8
67.4
13u
86.7
86.7
42.2
25.4
26.5
59.9
86.7
86.7
86.7
43.3
14u
94.1
94.1
65.3
29.0
24.2
94.1
94.1
94.1
94.1
49.0
15u
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
16U
88.4
88.4
88.4
88.4
88.4
88.4
88.4
88.4
88.4
88.4
1N
86.3
86.3
86.3
86.3
86.3
87.0
87.0
87.0
87.0
87.0
86.3 87.0
86.3 87.0
86.0
86.0
86.0
87.0 86.0
86.3 87.0
86.3
18U 19lJ
86.3 87.0 86.0
86.0
86.0
86.0
87.8
87.8
87.8
87.8
87.8
87.8
87.8
86.0 87.8
86.0
2ou
87.8
87.8
21u
88.6
88.6
42.9
83.4
82.8
86.6
88.6
88.6
88.6
73.1
22u
87.3
87.3
87.3
78.3
73.5
74.2
87.3
87.3
87.3
81.5
23U
86.1
86.1
86.1
65.1
65.2
78.3
86.1
86.1
86.1
77.2
24U
86.6
86.6
86.1
67.1
57.3
71.5
86.6
86.6
86.6
75.0
25U
91.5
91.5
83.8
49.4
48.4
71.4
91.5
91.5
91.5
68.7
26U
86.9
86.9
73.0
47.8
48.5
70.8
86.9
86.9
86.9
65.7
27U
95.9
95.9
79.1
62.4
41.5
66.4
86.7
95.9
95.9
68.2
28U
89.4
89.4
38.0
20.3
19.8
22.6
76.3
89.4
89.4
31.8
29U
95.8
95.9
95.9
95.9
95.9
95.9
87.0 89.7
87.0 89.7
95.9 87.0
95.9
87.0 89.7
95.9 87.0
95.9
3ou 31u
87.0
87.0
87.0
87.0
89.7
89.7
89.7
89.7
87.0 89.7
89.7
89.7
32U 5ou
95.9
95.9
95.9
95.9
95.9
95.9
95.9
95.8
95.9
95.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
.O
87.8
51u
87.1
87.1
87.1
87.1
87.1
87.1
87.1
87.1
.O
87.1
52U
88.6
88.6
88.6
88.6
88.6
88.6
88.6
88.6
.O
88.6
53u
86.1
86.1
86.1
86.1
86.1
86.1
86.1
86.1
86.1
86.1
54u
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
87.8
55u
88.8
88.8
88.8
88.8
88.8
88.8
88.8
88.8
88.8
88.8
56U
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0 84.8
57U
87.8
87.8
87.1
80.5
79.4
87.8
87.8
87.8
87.8
58U
94.1
94.1
39.2
26.1
79.0
94.1
94.1
94.1
94.1
52.5
59U
86.3
86.3
86.1
83.4
84.1
86.3
86.3
86.3
86.3
85.3
6OU
94.1
94.1
44.4
25.4
85.1
94.1
94.1
94.1
94.1
6lU
86.9
86.9
72.9
66.1
67.7
86.9
86.9
86.9
86.9
76.4
62U
87.5 87.3
87.5
86.4
67.1
62.9
87.5
87.5
87.5
77.3
87.3
78.3
56.0
60.7
78.5 71.4
87.3
87.3
87.3
72.3
88.1
88.1
41.5
28.1
37.0
62.4
88.1
88.1
88.1
48.3
63U 64U
54.6
65U
89.1
89.1
89.1
89.1
52.7
85.2
89.1
89.1
89.1
80.5
66u
90.9
90.9
90.9
90.9
30.7
76.0
90.9
90.9
90.9
69.6
67U
89.4
89.4
89.4
60.5
49.8
76.7
89.4
89.4
89.4
73.7
68u
87.5
87.5
85.3
58.4
35.7
49.4
87.5
87.5
87.5
61.9
69U
87.0
87.0
79.7
51.7
43.2
82.8
87.0
87.0
87.0
67.4
7ou
87.8
87.8
76.4
43.2
35.0
47.5
73.2
87.8
87.8
55.8
7lU
87.2
87.2
37.9
21.3
18.8
20.6
71.1
87.2
87.2
30.9
72U
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
86.9
86.9
86.9
.O
86.9
86.9
.O .O
86.9 90.0
8OU
86.9
86.9
86.9
86.9
86.9
8lU
86.9
90.0
86.9 90.0
86.9 90.0
86.9 90.0
83U
90.0 90.0
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86.9 90.0 90.0
90.0
90.0
90.0
90.0
90.0
90.0 90.0
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90.0
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90.0
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90.0
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90.0
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90.0
90.0
90.0
90.0
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90.0
90.0
90.0
90.0
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90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
90.0
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90.0
90.0
90.0
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90.0
90.0
90.0
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90.0
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51.4
51.4
51.4
51.4
51.4
51.4
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51.4
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50.0
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21.9
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50.0
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25.5
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51.4
51.4
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39.9
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43.2
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50.0
50.0
50.0
50.0
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51.4
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51.4
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51.4
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51.4 47.2
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51.4
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51.4 .O
51.4
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48.4 29.3
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51.4
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44.5
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150s
51.4
51.4
51.4
51.4
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51.4
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50.0
50.0
50.0
50.0
50.0
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50.0 50.0
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153s
51.4
51.4
51.4
49.1
50.2
51.4
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50.8
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50.0
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39.9
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51.4
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30.0
26.6
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32.9
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51.4
51.4
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17.0
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17.7
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50.4
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9.7
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51.4
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51.4
51.4
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43.3
43.3
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41.2
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51.4 41.7
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49.8
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49.2
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53.0
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51.3
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43.3
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49.8
49.2
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48.1
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46.2
46.2
46.2
46.2
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46.2
46.2
46.2
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46.2
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48.4
48.4
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48.4
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48.4
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46.2
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46.2
46.2
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42.9
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42.9
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33.2
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33.2
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34.7
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46.1
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46.1
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49.7
49.7
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49.7
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33.2
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33.8
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32.0
33.0
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19.6
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50.4
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50.9
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43.9
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43.4
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38.9
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32.0
27.2
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46.1
45.4
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44.7
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48.5
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37.9
36.9
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29.0
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48.5
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50.4
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50.4
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47.3
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47.3
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42.7
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42.7 50.3
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46.8
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43.9
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40.5
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43.6
34.4
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35.3
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33.8
33.8
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30.0
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46.7
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47.2
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47.2
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47.1
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49.2
48.0
47.8
48.0
48.0
48.1
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36.0
36.0
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50.6
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49.8
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48.6
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45.8
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52.1
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48.3
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37.1
37.1
.O
36.2
7605s
48.4
47.6
46.0
45.3
46.8
47.5
48.3
48.3
49.1
46.8
7606s
49.2
48.3
46.7
46.0
47.5
48.3
49.2
49.2
.O
47.3
7607s
51.2
50.7
50.7
50.1
50.1
50.6
51.2
51.2
.O
50.5
7612s
50.6
5-0.1
49.0
46.8
46.1
48.5
50.6
50.6
.O
48.0
7801s
38.8
34.9
34.9
32.7
32.2
34.9
38.8
38.8
.O
34.1
7803s
41.8
41.8
41.8
41.8
41.8
41.8
41.8
45.3
.O
42.1
8001s
48.8 38.9
48.8 38.8
46.9
46.7
48.8
38.8
38.8
38.8
48.8 38.8
48.8
8003s
48.8 38.9
.O 38.8
38.8
OOVERALL
75.4
72.6
58.8
45.0
43.8
56.4
74.4
83.6
56.1
38.8 75.9
13.8
47.8
